# kb

id: data-analysis-20260505-3
file: data/en.wikipedia.org/wiki/Data_analysis-3.md
category: reference
title: Data analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Data_analysis
date_saved: 2026-05-05T03:43:31.459045+00:00
instance: kb-cron
parent: data-analysis-20260505-0
summary:
=== Initial data analysis === The most important distinction between the initial data analysis phase and the main analysis phase is that during initial data analysis one refrains from any analysis that is aimed at answering the original research question. The initial data analysis phase is guided by the following four questions: ==== Quality of data ==== The quality of the data should be checked as early as possible. Data quality can be assessed in several ways, using different types of analysis: frequency counts, descriptive statistics (mean, standard deviation, median), normality (skewness, kurtosis, frequency histograms), normal imputation is needed. Analysis of extreme observations: outlying observations in the data are analyzed to see if they seem to disturb the distribution. Comparison and correction of differences in coding schemes: variables are compared with coding schemes of variables external to the data set, and possibly corrected if coding schemes are not comparable. Test for common-method variance. The choice of analyses to assess the data quality during the initial data analysis phase depends on the analyses that will be conducted in the main analysis phase. ==== Quality of measurements ==== The quality of the measurement instruments should only be checked during the initial data


id: data-analysis-20260505-4
file: data/en.wikipedia.org/wiki/Data_analysis-4.md
category: reference
title: Data analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Data_analysis
date_saved: 2026-05-05T03:43:31.459045+00:00
instance: kb-cron
parent: data-analysis-20260505-0
summary:
==== Exploratory and confirmatory approaches ==== In the main analysis phase, either an exploratory or confirmatory approach can be adopted. Usually the approach is decided before data is collected. In an exploratory analysis no clear hypothesis is stated before analysing the data, and the data is searched for models that describe the data well. In a confirmatory analysis, clear hypotheses about the data are tested. Exploratory data analysis should be interpreted carefully. When testing multiple models at once there is a high chance on finding at least one of them to be significant, but this can be due to a type 1 error. It is important to always adjust the significance level when testing multiple models with, for example, a Bonferroni correction. Also, one should not follow up an exploratory analysis with a confirmatory analysis in the same dataset. An exploratory analysis is used to find ideas for a theory, but not to test that theory as well. When a model is found exploratory in a dataset, then following up that analysis with a confirmatory analysis in the same dataset could simply mean that the results of the confirmatory analysis are due to the same type 1 error that resulted


id: data-sharing-20260505-0
file: data/en.wikipedia.org/wiki/Data_sharing-0.md
category: reference
title: Data sharing
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Data_sharing
date_saved: 2026-05-05T03:43:32.564033+00:00
instance: kb-cron
parent: data-sharing-20260505-0
summary:
Data sharing denotes the dissemination of research datasets to enable access and use by other investigators. Policies governing this practice are increasingly instituted by funding agencies, academic institutions, and scholarly journals, reflecting the consensus that transparency and openness constitute foundational principles of the scientific method. A number of funding agencies and science journals require authors of peer-reviewed papers to share any supplemental information (raw data, statistical methods or source code) necessary to understand, develop or reproduce published research. A great deal of scientific research is not subject to data sharing requirements, and many of these policies have liberal exceptions. In the absence of any binding requirement, data sharing is at the discretion of the scientists themselves. In addition, in certain situations governments and institutions prohibit or severely limit data sharing to protect proprietary interests, national security, and subject/patient/victim confidentiality. Data sharing may also be restricted to protect institutions and scientists from use of data for political purposes. Data and methods may be requested from an author years after publication. In order to encourage data sharing and prevent the loss or corruption of data, a number of funding agencies and journals established policies on data archiving. Access to publicly archived data


id: data-sharing-20260505-1
file: data/en.wikipedia.org/wiki/Data_sharing-1.md
category: reference
title: Data sharing
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Data_sharing
date_saved: 2026-05-05T03:43:32.564033+00:00
instance: kb-cron
parent: data-sharing-20260505-0
summary:
The idea is: An article about computational science in a scientific publication is not the scholarship itself, it is merely advertising of the scholarship. The actual scholarship is the complete software development environment and the complete set of instructions which generated the figures. The Data Observation Network for Earth (DataONE) and Data Conservancy are projects supported by the National Science Foundation to encourage and facilitate data sharing among research scientists and better support meta-analysis. In environmental sciences, the research community is recognizing that major scientific advances involving integration of knowledge in and across fields will require that researchers overcome not only the technological barriers to data sharing but also the historically entrenched institutional and sociological barriers. Dr. Richard J. Hodes, director of the National Institute on Aging has stated, "the old model in which researchers jealously guarded their data is no longer applicable". The Alliance for Taxpayer Access is a group of organizations that support open access to government sponsored research. The group has expressed a "Statement of Principles" explaining why they believe open access is important. They also list a number of international public access policies. This is no more so than in timely communication of essential information to


id: deductive-nomological-model-20260505-0
file: data/en.wikipedia.org/wiki/Deductive-nomological_model-0.md
category: reference
title: Deductive-nomological model
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Deductive-nomological_model
date_saved: 2026-05-05T03:43:33.815064+00:00
instance: kb-cron
parent: deductive-nomological-model-20260505-0
summary:
The deductive-nomological model (DN model) of scientific explanation, also known as Hempel's model, the Hempel–Oppenheim model, the Popper–Hempel model, or the covering law model, is a formal view of scientifically answering questions asking, "Why...?". The DN model poses scientific explanation as a deductive structure, one where truth of its premises entails truth of its conclusion, hinged on accurate prediction or postdiction of the phenomenon to be explained. Because of problems concerning humans' ability to define, discover, and know causality, this was omitted in initial formulations of the DN model. Causality was thought to be incidentally approximated by realistic selection of premises that derive the phenomenon of interest from observed starting conditions plus general laws. Still, the DN model formally permitted causally irrelevant factors. Also, derivability from observations and laws sometimes yielded absurd answers. When logical empiricism fell out of favor in the 1960s, the DN model was widely seen as a flawed or greatly incomplete model of scientific explanation. Nonetheless, it remained an idealized version of scientific explanation, and one that was rather accurate when applied to modern physics. In the early 1980s, a revision to the DN model emphasized maximal specificity for relevance of the conditions and axioms stated.


id: deductive-nomological-model-20260505-1
file: data/en.wikipedia.org/wiki/Deductive-nomological_model-1.md
category: reference
title: Deductive-nomological model
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Deductive-nomological_model
date_saved: 2026-05-05T03:43:33.815064+00:00
instance: kb-cron
parent: deductive-nomological-model-20260505-0
summary:
== Growth == Whereas Comtean positivism posed science as description, logical positivism emerged in the late 1920s and posed science as explanation, perhaps to better unify empirical sciences by covering not only fundamental science—that is, fundamental physics—but special sciences, too, such as biology, psychology, economics, and anthropology. After defeat of National Socialism with World War II's close in 1945, logical positivism shifted to a milder variant, logical empiricism. All variants of the movement, which lasted until 1965, are neopositivism, sharing the quest of verificationism. Neopositivists led emergence of the philosophy subdiscipline philosophy of science, researching such questions and aspects of scientific theory and knowledge. Scientific realism takes scientific theory's statements at face value, thus accorded either falsity or truth—probable or approximate or actual. Neopositivists held scientific antirealism as instrumentalism, holding scientific theory as simply a device to predict observations and their course, while statements on nature's unobservable aspects are elliptical at or metaphorical of its observable aspects, rather. DN model received its most detailed, influential statement by Carl G Hempel, first in his 1942 article "The function of general laws in history", and more explicitly with Paul Oppenheim in their 1948 article "Studies in the logic of explanation". Leading logical


id: deductive-nomological-model-20260505-2
file: data/en.wikipedia.org/wiki/Deductive-nomological_model-2.md
category: reference
title: Deductive-nomological model
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Deductive-nomological_model
date_saved: 2026-05-05T03:43:33.815064+00:00
instance: kb-cron
parent: deductive-nomological-model-20260505-0
summary:
== Weaknesses == By DN model, if one asks, "Why is that shadow 20 feet long?", another can answer, "Because that flagpole is 15 feet tall, the Sun is at x angle, and laws of electromagnetism". Yet by problem of symmetry, if one instead asked, "Why is that flagpole 15 feet tall?", another could answer, "Because that shadow is 20 feet long, the Sun is at x angle, and laws of electromagnetism", likewise a deduction from observed conditions and scientific laws, but an answer clearly incorrect. By the problem of irrelevance, if one asks, "Why did that man not get pregnant?", one could in part answer, among the explanans, "Because he took birth control pills"—if he factually took them, and the law of their preventing pregnancy—as covering law model poses no restriction to bar that observation from the explanans. Many philosophers have concluded that causality is integral to scientific explanation. DN model offers a necessary condition of a causal explanation—successful prediction—but not sufficient conditions of causal explanation, as a universal regularity can include spurious relations or simple correlations, for instance Z always following Y, but not Z because of Y, instead Y and then Z as an effect of X.


id: deductive-nomological-model-20260505-3
file: data/en.wikipedia.org/wiki/Deductive-nomological_model-3.md
category: reference
title: Deductive-nomological model
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Deductive-nomological_model
date_saved: 2026-05-05T03:43:33.815064+00:00
instance: kb-cron
parent: deductive-nomological-model-20260505-0
summary:
== Covering action == Through lawlike explanation, fundamental physics—often perceived as fundamental science—has proceeded through intertheory relation and theory reduction, thereby resolving experimental paradoxes to great historical success, resembling covering law model. In early 20th century, Ernst Mach as well as Wilhelm Ostwald had resisted Ludwig Boltzmann's reduction of thermodynamics—and thereby Boyle's law—to statistical mechanics partly because it rested on kinetic theory of gas, hinging on atomic/molecular theory of matter. Mach as well as Ostwald viewed matter as a variant of energy, and molecules as mathematical illusions, as even Boltzmann thought possible. In 1905, via statistical mechanics, Albert Einstein predicted the phenomenon Brownian motion—unexplained since reported in 1827 by botanist Robert Brown. Soon, most physicists accepted that atoms and molecules were unobservable yet real. Also in 1905, Einstein explained the electromagnetic field's energy as distributed in particles, doubted until this helped resolve atomic theory in the 1910s and 1920s. Meanwhile, all known physical phenomena were gravitational or electromagnetic, whose two theories misaligned. Yet belief in aether as the source of all physical phenomena was virtually unanimous. At experimental paradoxes, physicists modified the aether's hypothetical properties. Finding the luminiferous aether a useless hypothesis, Einstein in 1905 a priori unified all inertial


id: deductive-nomological-model-20260505-4
file: data/en.wikipedia.org/wiki/Deductive-nomological_model-4.md
category: reference
title: Deductive-nomological model
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Deductive-nomological_model
date_saved: 2026-05-05T03:43:33.815064+00:00
instance: kb-cron
parent: deductive-nomological-model-20260505-0
summary:
== Sources == Avent, Ryan, "The Q&A: Brian Greene—life after the Higgs", The Economist blog: Babbage, 19 Jul 2012. Ayala, Francisco J & Theodosius G Dobzhansky, eds, Studies in the Philosophy of Biology: Reduction and Related Problems (Berkeley & Los Angeles: University of California Press, 1974). Bechtel, William, Discovering Cell Mechanisms: The Creation of Modern Cell Biology (New York: Cambridge University Press, 2006). Bechtel, William, Philosophy of Science: An Overview for Cognitive Science (Hillsdale, NJ: Lawrence Erlbaum Associates, 1988). Bem, Sacha & Huib L de Jong, Theoretical Issues in Psychology: An Introduction, 2nd edn (London: Sage Publications, 2006). Ben-Menahem, Yemima, Conventionalism: From Poincaré to Quine (Cambridge: Cambridge University Press, 2006). Blackmore, J T & R Itagaki, S Tanaka, eds, Ernst Mach's Vienna 1895–1930: Or Phenomenalism as Philosophy of Science (Dordrecht: Kluwer Academic Publishers, 2001). Boffetta, Paolo, "Causation in the presence of weak associations", Critical Reviews in Food Science and Nutrition, 2010 Dec; 50(s1):13–16. Bolotin, David, An Approach to Aristotle's Physics: With Particular Attention to the Role of His Manner of Writing (Albany: State University of New York Press, 1998). Bourdeau, Michel, "Auguste Comte", in Edward N Zalta, ed, The Stanford Encyclopedia of Philosophy, Winter 2013 edn. Braibant, Sylvie & Giorgio


id: deductive-nomological-model-20260505-5
file: data/en.wikipedia.org/wiki/Deductive-nomological_model-5.md
category: reference
title: Deductive-nomological model
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Deductive-nomological_model
date_saved: 2026-05-05T03:43:33.815064+00:00
instance: kb-cron
parent: deductive-nomological-model-20260505-0
summary:
Rowlands, Peter, Oliver Lodge and the Liverpool Physical Society (Liverpool: Liverpool University Press, 1990). Sarkar, Sahotra & Jessica Pfeifer, eds, The Philosophy of Science: An Encyclopedia, Volume 1: A–M (New York: Routledge, 2006). Schwarz, John H (1998). "Recent developments in superstring theory". Proceedings of the National Academy of Sciences of the United States of America. 95 (6): 2750–7. Bibcode:1998PNAS...95.2750S. doi:10.1073/pnas.95.6.2750. PMC 19640. PMID 9501161. Schweber, Silvan S, QED and the Men who Made it: Dyson, Feynman, Schwinger, and Tomonaga (Princeton: Princeton University Press, 1994). Schliesser, Eric, "Hume's Newtonianism and anti-Newtonianism", in Edward N Zalta, ed, The Stanford Encyclopedia of Philosophy, Winter 2008 edn. Spohn, Wolfgang, The Laws of Belief: Ranking Theory and Its Philosophical Applications (Oxford: Oxford University Press, 2012). Suppe, Frederick, ed, The Structure of Scientific Theories, 2nd edn (Urbana, Illinois: University of Illinois Press, 1977). Tavel, Morton, Contemporary Physics and the Limits of Knowledge (Piscataway, NJ: Rutgers University Press, 2002). Torretti, Roberto, The Philosophy of Physics (New York: Cambridge University Press, 1999). Vongehr, Sascha, "Higgs discovery rehabilitating despised Einstein Ether", Science 2.0: Alpha Meme website, 13 Dec 2011. Vongehr, Sascha, "Supporting abstract relational space-time as fundamental without doctrinism against emergence, arXiv (History and Philosophy of Physics):0912.3069, 2 Oct


id: discovery-science-20260505-0
file: data/en.wikipedia.org/wiki/Discovery_science-0.md
category: reference
title: Discovery science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Discovery_science
date_saved: 2026-05-05T03:43:35.183336+00:00
instance: kb-cron
parent: discovery-science-20260505-0
summary:
Discovery science (also known as discovery-based science) is a scientific methodology which aims to find new patterns, correlations, and form hypotheses through the analysis of large-scale experimental data. The term “discovery science” encompasses various fields of study, including basic, translational, and computational science and research. Discovery-based methodologies are commonly contrasted with traditional scientific practice, the latter involving hypothesis formation before experimental data is closely examined. Discovery science involves the process of inductive reasoning or using observations to make generalisations, and can be applied to a range of science-related fields, e.g., medicine, proteomics, hydrology, psychology, and psychiatry. == Overview == === Purpose === Discovery science places an emphasis on 'basic' discovery, which can fundamentally change the status quo. For example, in the early years of water resources research, the use of discovery science was demonstrated by seeking to elucidate phenomena that were, until that point, unexplained. It did not matter how unusual these ideas may have been perceived to be. In this sense, discovery science is based on the attitude that "we must not allow our concepts of the earth, in so far as they transcend the reach of observation, to root themselves so deeply and so firmly in our minds


id: discovery-science-20260505-1
file: data/en.wikipedia.org/wiki/Discovery_science-1.md
category: reference
title: Discovery science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Discovery_science
date_saved: 2026-05-05T03:43:35.183336+00:00
instance: kb-cron
parent: discovery-science-20260505-0
summary:
=== Proteomics === Another example of discovery science is proteomics, a technology-driven and technology limited discovery science. Technologies for proteomic analysis provide information that is useful in discovery science. Proteome analysis as a discovery science is applicable in biotechnology, e.g., it assists in 1) the discovery of biochemical pathways which can identify targets for therapies, 2) developing new processes for manufacturing biological materials, 3) monitoring manufacturing processes for the purpose of quality control, and 4) developing diagnostic tests and efficacious treatment strategies for clinical diseases. In the context of proteomics, current life-science research remains technology-limited, however, recent available tools have assisted in evolving such research from being hypothesis-driven to discovery-driven. === Hydrology === Field hydrology has experienced a decline in progress due to a change from discovery-based field work to the gathering of data for modal parameterisation. In field hydrology, models are not any more useful than an understanding of how systems work, and discovery science allows for this understanding. Several important examples of field-based inquiry and discovery have taken place in field hydrology. These include: identifying spatial patterns of soil moisture and how they relate to topography; interrogating such data through the use of geostatistics; and discovering the importance


id: discovery-science-20260505-2
file: data/en.wikipedia.org/wiki/Discovery_science-2.md
category: reference
title: Discovery science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Discovery_science
date_saved: 2026-05-05T03:43:35.183336+00:00
instance: kb-cron
parent: discovery-science-20260505-0
summary:
== Methodology == Discovery-based methodologies are often viewed in contrast to traditional scientific practice, where hypotheses are formed before close examination of experimental data. However, from a philosophical perspective where all or most of the observable "low-hanging fruit" has already been plucked, examining the phenomenological world more closely than the senses alone (even augmented senses, e.g. via microscopes, telescopes, bifocals etc.) opens a new source of knowledge for hypothesis formation. This process is also known as inductive reasoning or the use of specific observations to make generalisations. Discovery science is usually a complex process, and consequently does not follow a simple linear cause and effect pattern. This means that outcomes are uncertain, and it is expected to have disappointing results as a fundamental part of discovery science. In particular, this may apply to medicine for the critically ill, where disease syndromes may be complex and multi-factorial. In psychiatry, studying complex relationships between brain and behaviour requires a large-scale science. This calls for a need to conceptually switch from hypothesis-driven studies to hypothesis-generating research which is discovery-based. Normally, discovery-based approaches for research are initially hypothesis-free, however, hypothesis testing can be elevated to a new level that effectively supports traditional hypothesis-driven studies.


id: evidence-based-practice-20260505-0
file: data/en.wikipedia.org/wiki/Evidence-based_practice-0.md
category: reference
title: Evidence-based practice
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Evidence-based_practice
date_saved: 2026-05-05T03:43:36.513768+00:00
instance: kb-cron
parent: evidence-based-practice-20260505-0
summary:
Evidence-based practice (EBP) is the idea that occupational practices ought to be based on scientific evidence. The movement towards evidence-based practices attempts to encourage and, in some instances, require professionals and other decision-makers to pay more attention to evidence to inform their decision-making. The goal of evidence-based practice is to eliminate unsound or outdated practices in favor of more-effective ones by shifting the basis for decision making from tradition, intuition, and unsystematic experience to firmly grounded scientific research. The proposal has been controversial, with some arguing that results may not specialize to individuals as well as traditional practices. Evidence-based practices have been gaining ground since the introduction of evidence-based medicine and have spread to the allied health professions, education, management, law, public policy, architecture, and other fields. In light of studies showing problems in scientific research (such as the replication crisis), there is also a movement to apply evidence-based practices in scientific research itself. Research into the evidence-based practice of science is called metascience. An individual or organisation is justified in claiming that a specific practice is evidence-based if, and only if, three conditions are met. First, the individual or organisation possesses comparative evidence about the effects of the specific


id: evidence-based-practice-20260505-1
file: data/en.wikipedia.org/wiki/Evidence-based_practice-1.md
category: reference
title: Evidence-based practice
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Evidence-based_practice
date_saved: 2026-05-05T03:43:36.513768+00:00
instance: kb-cron
parent: evidence-based-practice-20260505-0
summary:
Evaluating scientific research is extremely complex. The process can be greatly simplified with the use of a heuristic that ranks the relative strengths of results obtained from scientific research, which is called a hierarchy of evidence. The design of the study and the endpoints measured (such as survival or quality of life) affect the strength of the evidence. Typically, systematic reviews and meta-analysis rank at the top of the hierarchy while randomized controlled trials rank above observational studies, and expert opinion and case reports rank at the bottom. There is broad agreement on the relative strength of the different types of studies, but there is no single, universally-accepted hierarchy of evidence. More than 80 different hierarchies have been proposed for assessing medical evidence. == Applications == === Medicine === Evidence-based medicine is an approach to medical practice intended to optimize decision-making by emphasizing the use of evidence from well-designed and well-conducted research. Although all medicine based on science has some degree of empirical support, evidence-based medicine goes further, classifying evidence by its epistemologic strength and requiring that only the strongest types (coming from meta-analyses, systematic reviews, and randomized controlled trials) can yield strong recommendations; weaker types (such as from case-control


id: evidence-based-practice-20260505-2
file: data/en.wikipedia.org/wiki/Evidence-based_practice-2.md
category: reference
title: Evidence-based practice
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Evidence-based_practice
date_saved: 2026-05-05T03:43:36.513768+00:00
instance: kb-cron
parent: evidence-based-practice-20260505-0
summary:
==== Reviews of existing research on education ==== The following websites offer free analysis and information on education research: The Best Evidence Encyclopedia is a free website created by the Johns Hopkins University School of Education's Center for Data-Driven Reform in Education (established in 2004) and is funded by the Institute of Education Sciences, U.S. Department of Education. It gives educators and researchers reviews about the strength of the evidence supporting a variety of English programs available for students in grades K–12. The reviews cover programs in areas such as Mathematics, Reading, Writing, Science, Comprehensive school reform, and Early childhood Education; and include such topics as the effectiveness of technology and struggling readers. The Education Endowment Foundation was established in 2011 by The Sutton Trust, as a lead charity in partnership with Impetus Trust, together being the government-designated What Works Centre for UK Education. Evidence for the Every Student Succeeds Act began in 2017 and is produced by the Center for Research and Reform in Education at Johns Hopkins University School of Education. It offers free up-to-date information on current PK-12 programs in reading, writing, math, science, and others that meet the standards of the Every Student Succeeds Act (the


id: experimenter-s-regress-20260505-0
file: data/en.wikipedia.org/wiki/Experimenter's_regress-0.md
category: reference
title: Experimenter's regress
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Experimenter's_regress
date_saved: 2026-05-05T03:43:41.898956+00:00
instance: kb-cron
parent: experimenter-s-regress-20260505-0
summary:
In science, experimenter's regress refers to a loop of dependence between theory and evidence. In order to judge whether a new piece of evidence is correct we rely on theory-based predictions, and to judge the value of competing theories we rely on existing evidence. Cognitive bias affects experiments, and experiments determine which theory is valid. This issue is particularly important in new fields of science where there is no consensus regarding the values of various competing theories, and where the extent of experimental errors is not well known. If experimenter's regress acts a positive feedback system, it can be a source of pathological science. An experimenter's strong belief in a new theory produces confirmation bias, and any biased evidence they obtain then strengthens their belief in that particular theory. Neither individual researchers nor entire scientific communities are immune to this effect: see N-rays and polywater. Experimenter's regress is a typical relativistic phenomenon in the Empirical Programme of Relativism (EPOR). EPOR is very much concerned with a focus on social interactions, by looking at particular (local) cases and controversial issues in the context in which they happen. In EPOR, all scientific knowledge is perceived to be socially constructed and is thus


id: experimentum-crucis-20260505-0
file: data/en.wikipedia.org/wiki/Experimentum_crucis-0.md
category: reference
title: Experimentum crucis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Experimentum_crucis
date_saved: 2026-05-05T03:43:43.118696+00:00
instance: kb-cron
parent: experimentum-crucis-20260505-0
summary:
In science, an experimentum crucis (English: crucial experiment or critical experiment) is an experiment capable of decisively determining whether or not a particular hypothesis or theory is superior to all others whose acceptance is currently widespread in the scientific community. In particular, such an experiment—if true—must typically be able to produce a result that rules out all other hypotheses or theories, thereby demonstrating that under the conditions of the experiment (i.e., under the same external circumstances and for the same "input variables" within the experiment), those hypotheses and theories are proven false but the experimenter's hypothesis is not ruled out. An opposite view, rejecting the decisive value of the experimentum crucis in choosing one hypothesis or theory over its rivals, is the Duhem–Quine thesis. == History == Francis Bacon in his Novum Organum first described the concept of a situation in which one theory but not others would hold true, using the name instantia crucis ("crucial instance"). The phrase experimentum crucis, denoting the deliberate creation of such a situation for the purpose of testing the rival theories, was later coined by Robert Hooke and then famously used by Isaac Newton and Robert Boyle. The production of such an experiment is


id: expert-elicitation-20260505-0
file: data/en.wikipedia.org/wiki/Expert_elicitation-0.md
category: reference
title: Expert elicitation
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Expert_elicitation
date_saved: 2026-05-05T03:43:44.433509+00:00
instance: kb-cron
parent: expert-elicitation-20260505-0
summary:
In science, engineering, and research, expert elicitation is the synthesis of opinions of authorities of a subject where there is uncertainty due to insufficient data or when such data is unattainable because of physical constraints or lack of resources. Expert elicitation is essentially a scientific consensus methodology. It is often used in the study of rare events. Expert elicitation allows for parametrization, an "educated guess", for the respective topic under study. Expert elicitation generally helps quantify uncertainty. Expert elicitation tends to be multidisciplinary as well as interdisciplinary, with practically universal applicability, and is used in a broad range of fields. Prominent recent expert elicitation applications include climate change, modeling seismic hazard and damage, association of tornado damage to wind speed in developing the Enhanced Fujita scale, risk analysis for nuclear waste storage. In performing expert elicitation certain factors need to be taken into consideration. The topic must be one for which there are people who have predictive expertise. Furthermore, the objective should be to obtain an experts' carefully considered judgment based on a systematic consideration of all relevant evidence. For this reason one should take care to adopt strategies designed to help the expert being interviewed to avoid overlooking relevant


id: extended-peer-community-20260505-0
file: data/en.wikipedia.org/wiki/Extended_peer_community-0.md
category: reference
title: Extended peer community
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Extended_peer_community
date_saved: 2026-05-05T03:43:45.627385+00:00
instance: kb-cron
parent: extended-peer-community-20260505-0
summary:
The concept of Extended peer community belongs to the field of Sociology of science, and in particular the use of science in the solution of social, political or ecological problems. It was first introduced by in the 1990s by Silvio Funtowicz and Jerome R. Ravetz. in the context of what would become Post-normal science. An Extended peer community is intended by these authors as a space where both credentialed experts from different disciplines and lay stakeholders can discuss and deliberate. == Content == An Extended peer community is intended by its creators as an arrangement at the science policy interface that helps to expand and assess both the knowledge-base and the value-base of policy-making'. Post-normal science's extended peer community argues for two kind of extensions: first, more than one discipline is assumed to have a potential bearing on the issue being debated, thereby providing different lenses to consider the problem. Second the community is extended to lay actors, taken to be all those with stakes, or an interest, in the given issue. The lay members of the community thus constituted may also take upon themselves active 'research' tasks; this has happened e.g. in the so-called 'popular epidemiology', when the official


id: free-parameter-20260505-0
file: data/en.wikipedia.org/wiki/Free_parameter-0.md
category: reference
title: Free parameter
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Free_parameter
date_saved: 2026-05-05T03:43:46.862708+00:00
instance: kb-cron
parent: free-parameter-20260505-0
summary:
A free parameter is a variable in a mathematical model which cannot be predicted precisely or constrained by the model and must be estimated experimentally or theoretically. A mathematical model, theory, or conjecture is more likely to be right and less likely to be the product of wishful thinking if it relies on few free parameters and is consistent with large amounts of data. == See also == Decision variables Exogenous variables Overfitting Random variables State variables == References ==


id: fringe-science-20260505-0
file: data/en.wikipedia.org/wiki/Fringe_science-0.md
category: reference
title: Fringe science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Fringe_science
date_saved: 2026-05-05T03:43:48.153140+00:00
instance: kb-cron
parent: fringe-science-20260505-0
summary:
Fringe science refers to ideas whose attributes include being highly speculative or relying on premises already refuted. The chance of ideas rejected by editors and published outside the mainstream being correct is remote. When the general public does not distinguish between science and imitators, it risks exploitation, and in some cases, a "yearning to believe or a generalized suspicion of experts is a very potent incentive to accepting some pseudoscientific claims". The term "fringe science" covers everything from novel hypotheses, which can be tested utilizing the scientific method, to wild ad hoc hypotheses and mumbo jumbo. This has resulted in a tendency to dismiss all fringe science as the domain of pseudoscientists, hobbyists, and quacks. A concept that was once accepted by the mainstream scientific community may become fringe science because of a later evaluation of previous research. For example, focal infection theory, which held that focal infections of the tonsils or teeth are a primary cause of systemic disease, was once considered to be medical fact. It has since been dismissed because of a lack of evidence. == Description == The boundary between fringe science and pseudoscience is disputed. Friedlander writes that there is no widespread understanding of what


id: fringe-science-20260505-1
file: data/en.wikipedia.org/wiki/Fringe_science-1.md
category: reference
title: Fringe science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Fringe_science
date_saved: 2026-05-05T03:43:48.153140+00:00
instance: kb-cron
parent: fringe-science-20260505-0
summary:
== Responding to fringe science == Michael W. Friedlander has suggested some guidelines for responding to fringe science, which, he argues, is a more difficult problem than scientific misconduct. His suggested methods include impeccable accuracy, checking cited sources, not overstating orthodox science, thorough understanding of the Wegener continental drift example, examples of orthodox science investigating radical proposals, and prepared examples of errors from fringe scientists. Friedlander suggests that fringe science is necessary so mainstream science will not atrophy. Scientists must evaluate the plausibility of each new fringe claim, and certain fringe discoveries "will later graduate into the ranks of accepted" — while others "will never receive confirmation". Margaret Wertheim profiled many "outsider scientists" in her book Physics on the Fringe, who receive little or no attention from professional scientists. She describes all of them as trying to make sense of the world using the scientific method but in the face of being unable to understand modern science's complex theories. She also finds it fair that credentialed scientists do not bother spending a lot of time learning about and explaining problems with the fringe theories of uncredentialed scientists since the authors of those theories have not taken the time to understand


id: gold-effect-20260505-0
file: data/en.wikipedia.org/wiki/Gold_effect-0.md
category: reference
title: Gold effect
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Gold_effect
date_saved: 2026-05-05T03:43:49.268222+00:00
instance: kb-cron
parent: gold-effect-20260505-0
summary:
The Gold effect is the phenomenon in which a scientific idea, particularly in medicine, is developed to the status of an accepted position within a professional body or association by the social process itself of scientific conferences, committees, and consensus building, despite not being supported by conclusive evidence. The Gold effect is used to analyze errors in public health policy and practice, such as the widespread use of cholesterol screening in the prevention of cardiovascular disease. The effect was described by Thomas Gold in 1979. The effect was reviewed by Petr Skrabanek and James McCormick in their book Follies and Fallacies in Medicine. In their book, Skrabanek and McCormick describe the Gold effect as: "At the beginning a few people arrive at a state of near belief in some idea. A meeting is held to discuss the pros and cons of the idea. More people favouring the idea than those disinterested will be present. A representative committee will be nominated to prepare a collective volume to propagate and foster interest in the idea. The totality of resulting articles based on the idea will appear to show an increasing consensus. A specialized journal will be launched. Only orthodox or near orthodox


id: graphism-thesis-20260505-0
file: data/en.wikipedia.org/wiki/Graphism_thesis-0.md
category: reference
title: Graphism thesis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Graphism_thesis
date_saved: 2026-05-05T03:43:50.464721+00:00
instance: kb-cron
parent: graphism-thesis-20260505-0
summary:
In the sociology of science and science and technology studies (STS), the graphism thesis is a proposition advanced by Bruno Latour that visual inscriptions—especially graphs, diagrams, and other visual representations—are central to the practice and authority of science. First articulated in Latour's 1990 essay "Visualization and Cognition: Drawing Things Together", the thesis holds that the power of scientific disciplines is closely linked to their capacity to produce, circulate, and combine visual representations of data. The thesis has been empirically tested by a number of researchers, who have found strong correlations between the level of graph use in scientific publications and the perceived "hardness" of scientific disciplines. == Background == The graphism thesis emerged from Latour's broader theory of inscriptions in scientific practice, developed across several works including Laboratory Life (1979, with Steve Woolgar) and Science in Action (1987). In these works, Latour defined an inscription device as any apparatus or configuration that transforms a material substance into a visual display usable within a scientific text, such as a diagram, chart, photograph, map, equation, or table. Central to this framework is the concept of immutable mobiles (a key term in actor–network theory)—objects that can be transported across distances while retaining their


id: harking-20260505-0
file: data/en.wikipedia.org/wiki/HARKing-0.md
category: reference
title: HARKing
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/HARKing
date_saved: 2026-05-05T03:43:51.697921+00:00
instance: kb-cron
parent: harking-20260505-0
summary:
HARKing (hypothesizing after the results are known) is an acronym coined by social psychologist Norbert Kerr that refers to the questionable research practice of "presenting a post hoc hypothesis in the introduction of a research report as if it were an a priori hypothesis". Hence, a key characteristic of HARKing is that post hoc hypothesizing is falsely portrayed as a priori hypothesizing. HARKing may occur when a researcher tests an a priori hypothesis but then omits that hypothesis from their research report after they find out the results of their test. Post hoc analysis or post hoc theorizing then may lead to a post hoc hypothesis. == Types == Several types of HARKing have been distinguished, including: THARKing Transparently hypothesizing after the results are known, rather than the secretive, undisclosed, HARKing that was first proposed by Kerr. In this case, researchers openly declare that they developed their hypotheses after they observed their research results. CHARKing (or Pure HARKing) CHARKing or "pure HARKing" refers to the practice of constructing new hypotheses after the results are known and presenting them as a priori hypotheses. CHARKing is often regarded as the prototypical form of HARKing. RHARKing RHARKing refers to retrieving old hypotheses from


id: heterophenomenology-20260505-0
file: data/en.wikipedia.org/wiki/Heterophenomenology-0.md
category: reference
title: Heterophenomenology
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Heterophenomenology
date_saved: 2026-05-05T03:43:53.007645+00:00
instance: kb-cron
parent: heterophenomenology-20260505-0
summary:
In the thought of the philosopher Daniel Dennett, heterophenomenology ("phenomenology of another, not oneself") is an explicitly third-person, scientific approach to the study of consciousness and other mental phenomena. It consists of applying the scientific method with an anthropological bent, combining the subject's self-reports with all other available evidence to determine their mental state. The goal is to discover how subjects see the world themselves, without taking the accuracy of the subject's view for granted. == Overview == Heterophenomenology is put forth as the alternative to traditional Cartesian phenomenology, which Dennett calls "lone-wolf autophenomenology" to emphasize the fact that traditional phenomenology accepts the subject's self-reports as being authoritative. In contrast, heterophenomenology considers the subjects authoritative only about how things seem to them. It does not dismiss the Cartesian first-person perspective, but rather brackets it so that it can be intersubjectively verified by empirical means, allowing it to be submitted as scientific evidence. The method requires a researcher to listen to the subjects and take what they say seriously, but to also look at everything else available to them, including the subject's bodily responses and environment, evidence provided by relevant neurological or psychological studies, the researcher's memories of their own experiences,


id: hypothesis-20260505-0
file: data/en.wikipedia.org/wiki/Hypothesis-0.md
category: reference
title: Hypothesis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Hypothesis
date_saved: 2026-05-05T03:43:58.163409+00:00
instance: kb-cron
parent: hypothesis-20260505-0
summary:
A hypothesis (pl.: hypotheses) is a proposed explanation for a phenomenon. A scientific hypothesis must be based on observations and make a testable and reproducible prediction about reality, in a process beginning with an educated guess. If a hypothesis is repeatedly independently demonstrated by experiment to be true, it becomes a scientific theory. In colloquial usage, the words hypothesis and theory are often used interchangeably, but this is incorrect in the context of science. A working hypothesis is a provisionally-accepted hypothesis used for the purpose of pursuing further progress in research. Working hypotheses are frequently discarded, and often proposed with knowledge (and warning) that they are incomplete and thus false, with the intent of moving research in at least somewhat the right direction, especially when scientists are stuck on an issue and brainstorming ideas. In formal logic, a hypothesis is the antecedent in a proposition. For example, in the proposition "If P, then Q", statement P denotes the hypothesis (or antecedent) of the consequent Q. Hypothesis P is the assumption in a (possibly counterfactual) "what if" question. The adjective "hypothetical" (having the nature of a hypothesis or being assumed to exist as an immediate consequence of a hypothesis), can refer


id: hypothesis-20260505-1
file: data/en.wikipedia.org/wiki/Hypothesis-1.md
category: reference
title: Hypothesis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Hypothesis
date_saved: 2026-05-05T03:43:58.163409+00:00
instance: kb-cron
parent: hypothesis-20260505-0
summary:
A working hypothesis is a hypothesis that is provisionally accepted as a basis for further research in the hope that a tenable theory will be produced, even if the hypothesis ultimately fails. Like all hypotheses, a working hypothesis is constructed as a statement of expectations, which can be linked to the exploratory research purpose in empirical investigation. Working hypotheses are often used as a conceptual framework in qualitative research. The provisional nature of working hypotheses makes them useful as an organizing device in applied research. Here they act like a useful guide to address problems that are still in a formative phase. In recent years, philosophers of science have tried to integrate the various approaches to evaluating hypotheses, and the scientific method in general, to form a more complete system that integrates the individual concerns of each approach. Notably, Imre Lakatos and Paul Feyerabend, Karl Popper's colleague and student, respectively, have produced novel attempts at such a synthesis. == Hypotheses, concepts and measurement == Concepts in Hempel's deductive-nomological model play a key role in the development and testing of hypotheses. Most formal hypotheses connect concepts by specifying the expected relationships between propositions. When a set of hypotheses are grouped together,


id: hypothetico-deductive-model-20260505-0
file: data/en.wikipedia.org/wiki/Hypothetico-deductive_model-0.md
category: reference
title: Hypothetico-deductive model
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Hypothetico-deductive_model
date_saved: 2026-05-05T03:44:00.724162+00:00
instance: kb-cron
parent: hypothetico-deductive-model-20260505-0
summary:
The hypothetico-deductive model or method is a proposed description of the scientific method. According to it, scientific inquiry proceeds by formulating a hypothesis in a form that can be falsifiable, using a test on observable data where the outcome is not yet known. A test outcome that could have and does run contrary to predictions of the hypothesis is taken as a falsification of the hypothesis. A test outcome that could have, but does not run contrary to the hypothesis corroborates the theory. It is then proposed to compare the explanatory value of competing hypotheses by testing how stringently they are corroborated by their predictions. == Example == One example of an algorithmic statement of the hypothetico-deductive method is as follows: One possible sequence in this model would be 1, 2, 3, 4. If the outcome of 4 holds, and 3 is not yet disproven, you may continue with 3, 4, 1, and so forth; but if the outcome of 4 shows 3 to be false, you will have to go back to 2 and try to invent a new 2, deduce a new 3, look for 4, and so forth. Note that this method can never absolutely verify (prove


id: interdisciplinary-peer-review-20260505-0
file: data/en.wikipedia.org/wiki/Interdisciplinary_peer_review-0.md
category: reference
title: Interdisciplinary peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Interdisciplinary_peer_review
date_saved: 2026-05-05T03:44:02.046934+00:00
instance: kb-cron
parent: interdisciplinary-peer-review-20260505-0
summary:
Interdisciplinary Peer Review (IPR) is a peer review process with an additional focus outside of the area of the author's subject of expertise. Disciplines such as telecommunications, political science, engineering, and medicine require specific subject matter expertise, however, they still cross multiple disciplines and may require review from many alternate functional areas to achieve maximum perspective to prevent duplication or improper publication. Reviews of this nature may also cross cultures, race, and other demographics to gain perspective. == Reduplication and Interdisciplinarity == In Peer Commentary on Peer Review: A case study in scientific quality control, Stevan R. Harnard (p. 15) begins to touch on the concept by addressing how well the review process works and what factors prevent re-duplication. Julie Klein, a professor of interdisciplinary studies at Wayne State University, defined interdisciplinarity as "new divisions of intellectual labor, collaborative research, team teaching, hybrid fields, comparative studies, increased borrowing across disciplines, and a variety of unified, holistic perspectives that have created pressures upon traditional divisions of knowledge". Klein has also described how interdisciplinarity is used to "find answers to complex questions, address broad issues, explore disciplinary and professional relations, to solve problems beyond the scope of any one discipline, and to


id: interrogating-ethnography-20260505-0
file: data/en.wikipedia.org/wiki/Interrogating_Ethnography-0.md
category: reference
title: Interrogating Ethnography
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Interrogating_Ethnography
date_saved: 2026-05-05T03:44:03.389592+00:00
instance: kb-cron
parent: interrogating-ethnography-20260505-0
summary:
Interrogating Ethnography: Why Evidence Matters is a 2017 book by Steven Lubet of Northwestern University Law School, critiquing methods used in the discipline of ethnography. Writing in Contexts, Syid Ali of Long Island University called it "an essential critique of the most public-facing product sociology has to offer." Interrogating Ethnography, which criticizes ethnographers for the practice of changing the name of the area they study, is part of the Replication crisis. But it is largely a critique of ethnography methodology when it relies on the narratives of interviewees with no attempt to verify assertions of fact. Lubet began the project of writing this book after reading and publishing a notable critique of the use of evidence Alice Goffman's controversial 2014 book On the Run: Fugitive Life in an American City, That critique led him to read and attempt to verify "more than 50 ethnographic monographs and an equivalent number of articles. Focusing on sociologists’ studies of American cities... (and checking) facts that could be documented — or not... by consulting experts and pulling public records." According to the Chronicle of Higher Education, the book "has touched off a debate about what ethnographers might learn from legal scholars, and vice versa.


id: inverse-square-law-20260505-0
file: data/en.wikipedia.org/wiki/Inverse-square_law-0.md
category: reference
title: Inverse-square law
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Inverse-square_law
date_saved: 2026-05-05T03:44:04.680534+00:00
instance: kb-cron
parent: inverse-square-law-20260505-0
summary:
In physical science, an inverse-square law is any scientific law stating that the observed "intensity" of a specified physical quantity (being nothing more than the value of the physical quantity) is inversely proportional to the square of the distance from the source of that physical quantity. The fundamental cause for this can be understood as geometric dilution corresponding to point-source radiation into three-dimensional space. Radar energy expands during both the signal transmission and the reflected return, so the inverse square for both paths means that the radar will receive energy according to the inverse fourth power of the range. To prevent dilution of energy while propagating a signal, certain methods can be used such as a waveguide, which acts like a canal does for water, or how a gun barrel restricts hot gas expansion to one dimension in order to prevent loss of energy transfer to a bullet. == Formula == In mathematical notation the inverse square law can be expressed as an intensity (I) varying as a function of distance (d) from some centre. The intensity is proportional (see ∝) to the reciprocal of the square of the distance thus: intensity ∝ 1 distance 2 {\displaystyle {\text{intensity}}\ \propto \


id: inverse-square-law-20260505-1
file: data/en.wikipedia.org/wiki/Inverse-square_law-1.md
category: reference
title: Inverse-square law
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Inverse-square_law
date_saved: 2026-05-05T03:44:04.680534+00:00
instance: kb-cron
parent: inverse-square-law-20260505-0
summary:
=== Light and other electromagnetic radiation === The intensity (or illuminance or irradiance) of light or other linear waves radiating from a point source (energy per unit of area perpendicular to the source) is inversely proportional to the square of the distance from the source, so an object (of the same size) twice as far away receives only one-quarter the energy (in the same time period). More generally, the irradiance, i.e., the intensity (or power per unit area in the direction of propagation), of a spherical wavefront varies inversely with the square of the distance from the source (assuming there are no losses caused by absorption or scattering). For example, the intensity of radiation from the Sun is 9126 watts per square meter at the distance of Mercury (0.387 AU) but only 1367 watts per square meter at the distance of Earth (1 AU)—an approximate threefold increase in distance results in an approximate ninefold decrease in intensity of radiation. For non-isotropic radiators such as parabolic antennas, headlights, and lasers, the effective origin is located far behind the beam aperture. If you are close to the origin, you don't have to go far to double the radius, so the signal drops


id: inverse-square-law-20260505-2
file: data/en.wikipedia.org/wiki/Inverse-square_law-2.md
category: reference
title: Inverse-square law
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Inverse-square_law
date_saved: 2026-05-05T03:44:04.680534+00:00
instance: kb-cron
parent: inverse-square-law-20260505-0
summary:
== Non-Euclidean implications == The inverse-square law, fundamental in Euclidean spaces, also applies to non-Euclidean geometries, including hyperbolic space. The curvature present in these spaces alters physical laws, influencing a variety of fields such as cosmology, general relativity, and string theory. John D. Barrow, in his 2020 paper "Non-Euclidean Newtonian Cosmology," expands on the behavior of force (F) and potential (Φ) within hyperbolic 3-space (H3). He explains that F and Φ obey the relationships F ∝ 1 / R² sinh²(r/R) and Φ ∝ coth(r/R), where R represents the curvature radius and r represents the distance from the focal point. The concept of spatial dimensionality, first proposed by Immanuel Kant, remains a topic of debate concerning the inverse-square law. Dimitria Electra Gatzia and Rex D. Ramsier, in their 2021 paper, contend that the inverse-square law is more closely related to force distribution symmetry than to the dimensionality of space. In the context of non-Euclidean geometries and general relativity, deviations from the inverse-square law do not arise from the law itself but rather from the assumption that the force between two bodies is instantaneous, which contradicts special relativity. General relativity reinterprets gravity as the curvature of spacetime, leading particles to move along


id: isotope-dilution-20260505-0
file: data/en.wikipedia.org/wiki/Isotope_dilution-0.md
category: reference
title: Isotope dilution
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Isotope_dilution
date_saved: 2026-05-05T03:44:05.958864+00:00
instance: kb-cron
parent: isotope-dilution-20260505-0
summary:
Isotope dilution analysis is a method of determining the quantity of chemical substances. In its most simple conception, the method of isotope dilution comprises the addition of known amounts of isotopically enriched substance to the analyzed sample. Mixing of the isotopic standard with the sample effectively "dilutes" the isotopic enrichment of the standard and this forms the basis for the isotope dilution method. Isotope dilution is classified as a method of internal standardisation, because the standard (isotopically enriched form of analyte) is added directly to the sample. In addition, unlike traditional analytical methods which rely on signal intensity, isotope dilution employs signal ratios. Owing to both of these advantages, the method of isotope dilution is regarded among chemistry measurement methods of the highest metrological standing. Isotopes are variants of a particular chemical element which differ in neutron number. All isotopes of a given element have the same number of protons in each atom. The term isotope is formed from the Greek roots isos (ἴσος "equal") and topos (τόπος "place"), meaning "the same place"; thus, the meaning behind the name is that different isotopes of a single element occupy the same position on the periodic table. == Early history == Analytical


id: isotope-dilution-20260505-1
file: data/en.wikipedia.org/wiki/Isotope_dilution-1.md
category: reference
title: Isotope dilution
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Isotope_dilution
date_saved: 2026-05-05T03:44:05.958864+00:00
instance: kb-cron
parent: isotope-dilution-20260505-0
summary:
n A = n B R B − R A B R A B − R A × 1 + R A 1 + R B {\displaystyle n_{\mathrm {A} }=n_{\mathrm {B} }{\frac {R_{\mathrm {B} }-R_{\mathrm {AB} }}{R_{\mathrm {AB} }-R_{\mathrm {A} }}}\times {\frac {1+R_{\mathrm {A} }}{1+R_{\mathrm {B} }}}} In a typical gas chromatography analysis, isotopic dilution can decrease the uncertainty of the measurement results from 5% to 1%. It can also be used in mass spectrometry (commonly referred to as isotopic dilution mass spectrometry or IDMS), in which the isotopic ratio can be determined with precision typically better than 0.25%. === Optimum composition of the blend === In a simplified manner, the uncertainty of the measurement results is largely determined from the measurement of RAB: u ( n A ) 2 ∝ ( ∂ n A ∂ R A B ) 2 u ( R A B ) 2 = n A 2 ( R A − R B ) 2 ( R A − R A B ) 2 ( R A B − R B ) 2 u ( R A B ) 2 {\displaystyle u(n_{\mathrm {A} })^{2}\propto \left({\frac {\partial {n_{\mathrm {A} }}}{\partial R_{\mathrm {AB} }}}\right)^{2}u(R_{\mathrm {AB} })^{2}=n_{\mathrm {A}


id: jadad-scale-20260505-0
file: data/en.wikipedia.org/wiki/Jadad_scale-0.md
category: reference
title: Jadad scale
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Jadad_scale
date_saved: 2026-05-05T03:44:07.109883+00:00
instance: kb-cron
parent: jadad-scale-20260505-0
summary:
The Jadad scale, sometimes known as Jadad scoring or the Oxford quality scoring system, is a procedure to assess the methodological quality of a clinical trial by objective criteria. It is named after Canadian-Colombian physician Alex Jadad who in 1996 described a system for allocating such trials a score of between zero (very poor) and five (rigorous). It is the most widely used such assessment in the world, and as of May 2024, its seminal paper has been cited in over 25,000 scientific works. == Description == The Jadad scale independently assesses the methodological quality of a clinical trial judging the effectiveness of blinding. Alejandro "Alex" Jadad Bechara, a Colombian physician who worked as a Research Fellow at the Oxford Pain Relief Unit, Nuffield Department of Anaesthetics at the University of Oxford described the allocating trials a score of between zero (very poor) and five (rigorous) in an appendix to a 1996 paper. In a 2007 book, Jadad described the randomised controlled trial as "one of the simplest, most powerful and revolutionary forms of research". == Background == Clinical trials are conducted for the purpose of collecting data on the efficacy of medical treatments. The treatment might be, for example,


id: leiden-manifesto-20260505-0
file: data/en.wikipedia.org/wiki/Leiden_Manifesto-0.md
category: reference
title: Leiden Manifesto
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Leiden_Manifesto
date_saved: 2026-05-05T03:44:08.418093+00:00
instance: kb-cron
parent: leiden-manifesto-20260505-0
summary:
The Leiden Manifesto for research metrics (LM) is a list of "ten principles to guide research evaluation", published as a comment in Nature on 22 April 2015. It was formulated by public policy professor Diana Hicks, scientometrics professor Paul Wouters, and their colleagues at the 19th International Conference on Science and Technology Indicators, held between 3–5 September 2014 in Leiden, The Netherlands. The Leiden Manifesto was proposed as a guide to combat misuse of bibliometrics when evaluating scientific research literature. Examples of commonly used bibliometrics for science, or scientometrics, are the h-index, impact factor, other indicators such as Altmetrics. According to the Manifesto's authors, these metrics often pervasively misguide evaluations of scientific material. == Motivation == Motivations for codification of the Leiden Manifesto arose from a growing worry that "impact-factor obsession" was leading to inadequate judgement of scientific material that should be worthy of fair evaluation. Lead author Diana Hicks hoped that publishing the list in Nature would spread its ideas, already commonplace in the scientometrics sphere, to the broader scientific community. === DORA and other predecessors === The scientific community has long been interested in reforming assessment of the impact of scientific and academic research. The 2013 San Francisco


id: leiden-manifesto-20260505-1
file: data/en.wikipedia.org/wiki/Leiden_Manifesto-1.md
category: reference
title: Leiden Manifesto
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Leiden_Manifesto
date_saved: 2026-05-05T03:44:08.418093+00:00
instance: kb-cron
parent: leiden-manifesto-20260505-0
summary:
=== Public endorsement === LIBER, a collaboration of European research libraries, issued a substantial review of the Leiden Manifesto in 2017, concluding that it was a "solid foundation" on which academic libraries could base their assessment of metrics. Elsevier, a global leader in research publishing and information analytics, announced on 14 July 2020 that it would use the Leiden Manifesto to guide its development of improved research evaluation. Elsevier stated that the principles of the manifesto were already close in nature to their 2019 CiteScore metrics, which was in summary "improved calculation methodology" for "a more robust, fair and faster indicator of research impact". Loughborough University's LIS-Bibliometrics committee chose to base their own principles on the Leiden Manifesto, instead of the DORA, because the manifesto takes a "broader approach to the responsible use of all bibliometrics across a range of disciplines and settings", according to their policy manager Elizabeth Gadd. Stephen Curry, chair of the DORA steering committee, commented on this statement by emphasizing that DORA was aiming to extend its "disciplinary and geographical reach". === Further applications === David Moher and his co-authors referenced the Leiden Manifesto in a perspective for Issues in Science and Technology, writing that academic


id: level-of-measurement-20260505-0
file: data/en.wikipedia.org/wiki/Level_of_measurement-0.md
category: reference
title: Level of measurement
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Level_of_measurement
date_saved: 2026-05-05T03:44:10.167435+00:00
instance: kb-cron
parent: level-of-measurement-20260505-0
summary:
Level of measurement or scale of measure is a classification that describes the nature of information within the values assigned to variables. Psychologist Stanley Smith Stevens developed the best-known classification with four levels, or scales, of measurement: nominal, ordinal, interval, and ratio. This framework of distinguishing levels of measurement originated in psychology and has since had a complex history, being adopted and extended in some disciplines and by some scholars, and criticized or rejected by others. Other classifications include those by Mosteller and Tukey, and by Chrisman. Stevens proposed his typology in a 1946 Science article titled "On the theory of scales of measurement". In that article, Stevens claimed that all measurement in science was conducted using four different types of scales that he called "nominal", "ordinal", "interval", and "ratio", unifying both "qualitative" (which are described by his "nominal" type) and "quantitative" (to a different degree, all the rest of his scales). The concept of scale types later received the mathematical rigour that it lacked at its inception with the work of mathematical psychologists Theodore Alper (1985, 1987), Louis Narens (1981a, b), and R. Duncan Luce (1986, 1987, 2001). As Luce (1997, p. 395) wrote: S. S. Stevens (1946, 1951,


id: level-of-measurement-20260505-1
file: data/en.wikipedia.org/wiki/Level_of_measurement-1.md
category: reference
title: Level of measurement
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Level_of_measurement
date_saved: 2026-05-05T03:44:10.167435+00:00
instance: kb-cron
parent: level-of-measurement-20260505-0
summary:
==== Central tendency and dispersion ==== According to Stevens, for ordinal data, the appropriate measure of central tendency is the median (the mode is also allowed, but not the mean), and the appropriate measure of dispersion is percentile or quartile (the standard deviation is not allowed). Those restrictions would imply that correlations can only be evaluated using rank order methods, and statistical significance can only be evaluated using non-parametric methods (R. M. Kothari, 2004). But the restrictions have not been generally endorsed by statisticians. In 1946, Stevens observed that psychological measurement, such as measurement of opinions, usually operates on ordinal scales; thus means and standard deviations have no validity according to his rules, but they can be used to get ideas for how to improve operationalization of variables used in questionnaires. Indeed, most psychological data collected by psychometric instruments and tests, measuring cognitive and other abilities, are ordinal (Cliff, 1996; Cliff & Keats, 2003; Michell, 2008). In particular, IQ scores reflect an ordinal scale, in which all scores are meaningful for comparison only. There is no zero point that represents an absence of intelligence, and a 10-point difference may carry different meanings at different points of the scale. === Interval


id: level-of-measurement-20260505-2
file: data/en.wikipedia.org/wiki/Level_of_measurement-2.md
category: reference
title: Level of measurement
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Level_of_measurement
date_saved: 2026-05-05T03:44:10.167435+00:00
instance: kb-cron
parent: level-of-measurement-20260505-0
summary:
== Debate over Stevens's typology == While Stevens's typology is widely adopted, it is still being challenged by other theoreticians, particularly in the cases of the nominal and ordinal types (Michell, 1986). Duncan (1986), for example, objected to the use of the word measurement in relation to the nominal type and Luce (1997) disagreed with Stevens's definition of measurement. On the other hand, Stevens (1975) said of his own definition of measurement that "the assignment can be any consistent rule. The only rule not allowed would be random assignment, for randomness amounts in effect to a nonrule". Hand says, "Basic psychology texts often begin with Stevens's framework and the ideas are ubiquitous. Indeed, the essential soundness of his hierarchy has been established for representational measurement by mathematicians, determining the invariance properties of mappings from empirical systems to real number continua. Certainly the ideas have been revised, extended, and elaborated, but the remarkable thing is his insight given the relatively limited formal apparatus available to him and how many decades have passed since he coined them." The use of the mean as a measure of the central tendency for the ordinal type is still debatable among those who accept Stevens's typology.


id: level-of-measurement-20260505-3
file: data/en.wikipedia.org/wiki/Level_of_measurement-3.md
category: reference
title: Level of measurement
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Level_of_measurement
date_saved: 2026-05-05T03:44:10.167435+00:00
instance: kb-cron
parent: level-of-measurement-20260505-0
summary:
…any law purporting to express a quantitative relation between sensation intensity and stimulus intensity is not merely false but is in fact meaningless unless and until a meaning can be given to the concept of addition as applied to sensation. That is, if Stevens's sone scale genuinely measured the intensity of auditory sensations, then evidence for such sensations as being quantitative attributes needed to be produced. The evidence needed was the presence of additive structure—a concept comprehensively treated by the German mathematician Otto Hölder (Hölder, 1901). Given that the physicist and measurement theorist Norman Robert Campbell dominated the Ferguson committee's deliberations, the committee concluded that measurement in the social sciences was impossible due to the lack of concatenation operations. This conclusion was later rendered false by the discovery of the theory of conjoint measurement by Debreu (1960) and independently by Luce & Tukey (1964). However, Stevens's reaction was not to conduct experiments to test for the presence of additive structure in sensations, but instead to render the conclusions of the Ferguson committee null and void by proposing a new theory of measurement: Paraphrasing N. R. Campbell (Final Report, p. 340), we may say that measurement, in the broadest sense, is


id: list-of-environmental-sampling-techniques-20260505-0
file: data/en.wikipedia.org/wiki/List_of_environmental_sampling_techniques-0.md
category: reference
title: List of environmental sampling techniques
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/List_of_environmental_sampling_techniques
date_saved: 2026-05-05T03:44:11.358834+00:00
instance: kb-cron
parent: list-of-environmental-sampling-techniques-20260505-0
summary:
Environmental sampling techniques are used in biology, ecology and conservation as part of scientific studies to learn about the flora and fauna of a particular area and establish a habitat's biodiversity, the abundance of species and the conditions in which these species live amongst other information. Where species are caught, researchers often then take the trapped organisms for further study in a lab or are documented by a researcher in the field before the animal is released. This information can then be used to better understand the environment, its ecology, the behaviour of species and how organisms interact with one another and their environment. Here is a list of some sampling techniques and equipment used in environmental sampling: Quadrats - used for plants and slow moving animals == Techniques for birds and/or flying invertebrates and/or bats == Malaise Trap Flight Interception Trap Harp Trap Robinson Trap Butterfly Net Mist Net == Techniques for Terrestrial Animals == Transect Tullgren Funnel - used for soil-living arthropods Pitfall Trap - used for small terrestrial animals like insects and amphibians Netting techniques for terrestrial animals Beating Net - used for insects dwelling in trees and shrubs Sweep Netting - used for insects in grasses


id: list-of-mathematics-based-methods-20260505-0
file: data/en.wikipedia.org/wiki/List_of_mathematics-based_methods-0.md
category: reference
title: List of mathematics-based methods
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/List_of_mathematics-based_methods
date_saved: 2026-05-05T03:44:16.378190+00:00
instance: kb-cron
parent: list-of-mathematics-based-methods-20260505-0
summary:
This is a list of mathematics-based methods. Adams' method (differential equations) Akra–Bazzi method (asymptotic analysis) Bisection method (root finding) Brent's method (root finding) Condorcet method (voting systems) Coombs' method (voting systems) Copeland's method (voting systems) Crank–Nicolson method (numerical analysis) D'Hondt method (voting systems) D21 – Janeček method (voting system) Discrete element method (numerical analysis) Domain decomposition method (numerical analysis) Epidemiological methods Euler's forward method Explicit and implicit methods (numerical analysis) Finite difference method (numerical analysis) Finite element method (numerical analysis) Finite volume method (numerical analysis) Highest averages method (voting systems) Method of exhaustion Method of infinite descent (number theory) Information bottleneck method Inverse chain rule method (calculus) Inverse transform sampling method (probability) Iterative method (numerical analysis) Jacobi method (linear algebra) Largest remainder method (voting systems) Level-set method Linear combination of atomic orbitals molecular orbital method (molecular orbitals) Method of characteristics Least squares method (optimization, statistics) Maximum likelihood method (statistics) Method of complements (arithmetic) Method of moving frames (differential geometry) Method of successive substitution (number theory) Monte Carlo method (computational physics, simulation) Newton's method (numerical analysis) Pemdas method (order of operation) Perturbation methods (functional analysis, quantum theory) Probabilistic method (combinatorics) Romberg's method (numerical analysis) Runge–Kutta method (numerical analysis) Sainte-Laguë method (voting


id: mature-technology-20260505-0
file: data/en.wikipedia.org/wiki/Mature_technology-0.md
category: reference
title: Mature technology
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Mature_technology
date_saved: 2026-05-05T03:44:17.608761+00:00
instance: kb-cron
parent: mature-technology-20260505-0
summary:
A mature technology is a technology that has been in use for long enough that most of its initial faults and inherent problems have been removed or reduced by further development. In some contexts, it may also refer to technology that has not seen widespread use, but whose scientific background is well understood. Its performance characteristics are also expected to be well understood with well-established design specifications. One of the key indicators of a mature technology is the ease of use for both non-experts and professionals. Another indicator is a reduction in the rate of new breakthrough advances related to it—whereas inventions related to a (popular) immature technology are usually rapid and diverse, and may change the whole use paradigm—advances to a mature technology are usually incremental improvements only. == Examples == The QWERTY keyboard design is an example of mature technology because its performance characteristics such as typing speeds and error rates have been established in various describable situations. Additionally, the basic key organization of this technology has remained the same over the last century. Another example is the barcode, a technology that also satisfies all the previously cited indicators. It is widely used since when it was first


id: mertonian-norms-20260505-0
file: data/en.wikipedia.org/wiki/Mertonian_norms-0.md
category: reference
title: Mertonian norms
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Mertonian_norms
date_saved: 2026-05-05T03:44:18.741729+00:00
instance: kb-cron
parent: mertonian-norms-20260505-0
summary:
In 1942, Robert K. Merton described four aspects of science that later came to be called Mertonian norms: "four sets of institutional imperatives taken to comprise the ethos of modern science... communism, universalism, disinterestedness, and organized skepticism". The subsequent portion of his book, The Sociology of Science, elaborated on these principles at "the heart of the Mertonian paradigm—the powerful juxtaposition of the normative structure of science with its institutionally distinctive reward system". == Description and motivation == Merton defines this 'ethos' with reference to Albert Bayet's 1931 work La Morale de la Science, which "abandons description and analysis for homily" as "that affectively toned complex of values and norms which is held to be binding on the man of science". He attempted to clarify it, given that previously it had not been 'codified'; Merton uses Bayet's remark that 'this scientific ethos [morale] does not have its theoreticians, but it has its artisans. It does not express its ideals, but serves them: it is implicated in the very existence of science'. The norms are expressed in the form of prescriptions, proscriptions, preferences, and permissions. They are legitimatized in terms of institutional values. These imperatives, transmitted by precept and example and reenforced


id: mertonian-norms-20260505-1
file: data/en.wikipedia.org/wiki/Mertonian_norms-1.md
category: reference
title: Mertonian norms
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Mertonian_norms
date_saved: 2026-05-05T03:44:18.741729+00:00
instance: kb-cron
parent: mertonian-norms-20260505-0
summary:
Secondly, to restrict scientific careers for any reason other than incompetence was to "prejudice the furtherance of knowledge". Merton again noted how the ethos of science may be inconsistent with that of society, but insists that "however inadequately it may be put into practice, the ethos of democracy includes universalism as a dominant guiding principle". He predicted that this inadequacy of laissez-faire democratic processes would lead ultimately to false differential accumulation and increasing regulation of science under political authority, which must be counteracted through "new technical forms of organization" towards equality of opportunity. === Disinterestedness === Distinct from altruism, scientists should act for the benefit of a common scientific enterprise rather than for specific outcomes. Merton reasoned that an individual's scientific motivation may be easily influenced and without institutional enforcement of disinterestedness, and the "seeming virtual absence of fraud" could not be explained by unusually high moral integrity of individuals alone. Merton observed a low rate of fraud in science ("virtual absence … which appears exceptional"), which he believed stemmed from the intrinsic need for "verifiability" in science and expert scrutiny by peers ("rigorous policing, to a degree perhaps unparalleled in any other field of activity") as well as the


id: meta-research-center-at-tilburg-university-20260505-0
file: data/en.wikipedia.org/wiki/Meta-Research_Center_at_Tilburg_University-0.md
category: reference
title: Meta-Research Center at Tilburg University
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Meta-Research_Center_at_Tilburg_University
date_saved: 2026-05-05T03:44:19.886629+00:00
instance: kb-cron
parent: meta-research-center-at-tilburg-university-20260505-0
summary:
The Meta-Research Center at Tilburg University is a metascience research center within the School of Social and Behavioral Sciences at the Dutch Tilburg University. They were profiled in a September 2018 article in Science. == Research == Meta-research aims to improve reproducibility by studying how science is practiced and published and developing better ways for the scientific community to operate. The research institute has published a large statistical meta-analysis of studies on the effect of Stereotype threat on girls' mathematics performance. They also use methods for estimating publication bias. The research institute has developed an R based software tool called Statcheck that can detect incorrect statistical methods used in research articles. A large amount of statistical errors were detected in a sample of 50 000 psychology research articles. The use of it was perceived negatively by some of the researchers. The data mining practices of the research center have been in conflict with the policies of scientific publisher Elsevier. A scientific misconduct case in the field of social psychology at Tilburg University has been a contributing factor in establishing the research center. == Advocacy == The research center makes recommendations for other researchers about how to avoid publication bias and


id: metascience-20260505-0
file: data/en.wikipedia.org/wiki/Metascience-0.md
category: reference
title: Metascience
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Metascience
date_saved: 2026-05-05T03:44:21.146005+00:00
instance: kb-cron
parent: metascience-20260505-0
summary:
Metascience (also known as meta-research) is the use of scientific methodology to study science itself. Metascience seeks to increase the quality of scientific research and enhance its efficiency. It is also known as "research on research" and "the science of science", as it uses research methods to study how research is done and find where improvements can be made. Metascience concerns itself with all fields of research and has been described as "a bird's eye view of science". In the words of John Ioannidis, "Science is the best thing that has happened to human beings ... but we can do it better." In 1966, an early meta-research paper examined the statistical methods of 295 papers published in ten high-profile medical journals. It found that "in almost 73% of the reports read ... conclusions were drawn when the justification for these conclusions was invalid." Meta-research in the following decades found many methodological flaws, inefficiencies, and poor practices in research across numerous scientific fields. Many scientific studies could not be reproduced, particularly in medicine and the soft sciences. The term "replication crisis" was coined in the early 2010s as part of a growing awareness of the problem. Measures have been implemented to


id: metascience-20260505-1
file: data/en.wikipedia.org/wiki/Metascience-1.md
category: reference
title: Metascience
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Metascience
date_saved: 2026-05-05T03:44:21.146005+00:00
instance: kb-cron
parent: metascience-20260505-0
summary:
=== Reporting === Meta-research has identified poor practices in reporting, explaining, disseminating and popularizing research, particularly within the social and health sciences. Poor reporting makes it difficult to accurately interpret the results of scientific studies, to replicate studies, and to identify biases and conflicts of interest in the authors. Solutions include the implementation of reporting standards, and greater transparency in scientific studies (including better requirements for disclosure of conflicts of interest). There is an attempt to standardize reporting of data and methodology through the creation of guidelines by reporting agencies such as CONSORT and the larger EQUATOR Network. === Reproducibility === The replication crisis is an ongoing methodological crisis in which it has been found that many scientific studies are difficult or impossible to replicate. While the crisis has its roots in the meta-research of the mid- to late 20th century, the phrase "replication crisis" was not coined until the early 2010s as part of a growing awareness of the problem. The replication crisis has been closely studied in psychology (especially social psychology) and medicine, including cancer research. Replication is an essential part of the scientific process, and the widespread failure of replication puts into question the reliability of affected


id: metascience-20260505-2
file: data/en.wikipedia.org/wiki/Metascience-2.md
category: reference
title: Metascience
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Metascience
date_saved: 2026-05-05T03:44:21.146005+00:00
instance: kb-cron
parent: metascience-20260505-0
summary:
===== Science funding and awards ===== Scientific awards are one category of science incentives. Metascience can explore existing and hypothetical systems of science awards. For instance, it found that work honored by Nobel Prizes clustered in only a few scientific fields with only 36/71 having received at least one Nobel Prize. Of the 114/849 domains science could be divided into their DC2 and DC3 classification systems, five were shown to comprise over half of the Nobel Prizes awarded between 1995 and 2017 (particle physics [14%], cell biology [12.1%], atomic physics [10.9%], neuroscience [10.1%], molecular chemistry [5.3%]). A study found that delegation of responsibility by policy-makers – a centralized authority-based top-down approach – for knowledge production and appropriate funding to science with science subsequently somehow delivering "reliable and useful knowledge to society" is too simple. Measurements show that allocation of bio-medical resources can be more strongly correlated to previous allocations and research than to burden of diseases. A study suggests that "[i]f peer review is maintained as the primary mechanism of arbitration in the competitive selection of research reports and funding, then the scientific community needs to make sure it is not arbitrary". Studies indicate there to is a need to


id: metascience-20260505-3
file: data/en.wikipedia.org/wiki/Metascience-3.md
category: reference
title: Metascience
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Metascience
date_saved: 2026-05-05T03:44:21.146005+00:00
instance: kb-cron
parent: metascience-20260505-0
summary:
Metascience can investigate how scientific processes evolve over time. A study found that teams are growing in size, "increasing by an average of 17% per decade". (see labor advantage below) It was found that prevalent forms of non-open access publication and prices charged for many conventional journals – even for publicly funded papers – are unwarranted, unnecessary – or suboptimal – and detrimental barriers to scientific progress. Open access can save considerable amounts of financial resources, which could be used otherwise, and level the playing field for researchers in developing countries. There are substantial expenses for subscriptions, gaining access to specific studies, and for article processing charges. Paywall: The Business of Scholarship is a documentary on such issues. Another topic are the established styles of scientific communication (e.g. long text-form studies and reviews) and the scientific publishing practices – there are concerns about a "glacial pace" of conventional publishing. The use of preprint-servers to publish study-drafts early is increasing and open peer review, new tools to screen studies, and improved matching of submitted manuscripts to reviewers are among the proposals to speed up publication. ==== Science overall and intrafield developments ==== Metadata from research publications can be extracted, enriched, and


id: metascience-20260505-4
file: data/en.wikipedia.org/wiki/Metascience-4.md
category: reference
title: Metascience
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Metascience
date_saved: 2026-05-05T03:44:21.146005+00:00
instance: kb-cron
parent: metascience-20260505-0
summary:
Some research is investigating scientific controversy or controversies, and may identify currently ongoing major debates (e.g. open questions), and disagreement between scientists or studies. One study suggests the level of disagreement was highest in the social sciences and humanities (0.61%), followed by biomedical and health sciences (0.41%), life and earth sciences (0.29%); physical sciences and engineering (0.15%), and mathematics and computer science (0.06%). Such research may also show, where the disagreements are, especially if they cluster, including visually such as with cluster diagrams. === Challenges of interpretation of pooled results === Studies about a specific research question or research topic are often reviewed in the form of higher-level overviews in which results from various studies are integrated, compared, critically analyzed and interpreted. Examples of such works are scientific reviews and meta-analyses. These and related practices face various challenges and are a subject of metascience. Various issues with available studies such as, for example, heterogeneity of methods used may lead to faulty conclusions of the meta-analysis. === Knowledge integration and living documents === Various problems require swift integration of new and existing science-based knowledge. Especially setting where there are a large number of loosely related projects and initiatives benefit from a


id: metascience-20260505-5
file: data/en.wikipedia.org/wiki/Metascience-5.md
category: reference
title: Metascience
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Metascience
date_saved: 2026-05-05T03:44:21.146005+00:00
instance: kb-cron
parent: metascience-20260505-0
summary:
==== As and for acceleration of progress ==== A study suggests that improving the way science is done could accelerate the rate of scientific discovery and its applications which could be useful for finding urgent solutions to humanity's problems, improve humanity's conditions, and enhance understanding of nature. Metascientific studies can seek to identify aspects of science that need improvement, and develop ways to improve them. If science is accepted as the fundamental engine of economic growth and social progress, this could raise "the question of what we – as a society – can do to accelerate science, and to direct science toward solving society's most important problems." One author clarified that funding science requires diverse approaches rather than a single model. DARPA models, curiosity-driven research, peer-champion mechanisms, and other approaches each have merit and application in different contexts. Nevertheless, evaluation of them can help build knowledge of what works or works best. == Reforms == Meta-research identifying flaws in scientific practice has inspired reforms in science. These reforms seek to address and fix problems in scientific practice which lead to low-quality or inefficient research. A 2015 study lists "fragmented" efforts in meta-research. === Pre-registration === The practice of registering a


id: metascience-20260505-6
file: data/en.wikipedia.org/wiki/Metascience-6.md
category: reference
title: Metascience
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Metascience
date_saved: 2026-05-05T03:44:21.146005+00:00
instance: kb-cron
parent: metascience-20260505-0
summary:
Clinical research in medicine is often of low quality, and many studies cannot be replicated. An estimated 85% of research funding is wasted. Additionally, the presence of bias affects research quality. The pharmaceutical industry exerts substantial influence on the design and execution of medical research. Conflicts of interest are common among authors of medical literature and among editors of medical journals. While almost all medical journals require their authors to disclose conflicts of interest, editors are not required to do so. Financial conflicts of interest have been linked to higher rates of positive study results. In antidepressant trials, pharmaceutical sponsorship is the best predictor of trial outcome. Blinding is another focus of meta-research, as error caused by poor blinding is a source of experimental bias. Blinding is not well reported in medical literature, and widespread misunderstanding of the subject has resulted in poor implementation of blinding in clinical trials. Furthermore, failure of blinding is rarely measured or reported. Research showing the failure of blinding in antidepressant trials has led some scientists to argue that antidepressants are no better than placebo. In light of meta-research showing failures of blinding, CONSORT standards recommend that all clinical trials assess and report the quality


id: list-of-multiple-discoveries-20260505-0
file: data/en.wikipedia.org/wiki/List_of_multiple_discoveries-0.md
category: reference
title: List of multiple discoveries
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/List_of_multiple_discoveries
date_saved: 2026-05-05T03:44:22.470419+00:00
instance: kb-cron
parent: list-of-multiple-discoveries-20260505-0
summary:
Historians and sociologists have remarked the occurrence, in science, of "multiple independent discovery". Robert K. Merton defined such "multiples" as instances in which similar discoveries are made by scientists working independently of each other. "Sometimes", writes Merton, "the discoveries are simultaneous or almost so; sometimes a scientist will make a new discovery which, unknown to him, somebody else has made years before." Commonly cited examples of multiple independent discovery are the 17th-century independent formulation of calculus by Isaac Newton and Gottfried Wilhelm Leibniz; the 18th-century discovery of oxygen by Carl Wilhelm Scheele, Joseph Priestley, Antoine Lavoisier and others; and the theory of the evolution of species, independently advanced in the 19th century by Charles Darwin and Alfred Russel Wallace. Multiple independent discovery, however, is not limited to such famous historic instances. Merton believed that it is multiple discoveries, rather than unique ones, that represent the common pattern in science. Merton contrasted a "multiple" with a "singleton"—a discovery that has been made uniquely by a single scientist or group of scientists working together. The distinction may blur as science becomes increasingly collaborative. A distinction is drawn between a discovery and an invention, as discussed for example by Bolesław Prus. However, discoveries


id: list-of-multiple-discoveries-20260505-1
file: data/en.wikipedia.org/wiki/List_of_multiple_discoveries-1.md
category: reference
title: List of multiple discoveries
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/List_of_multiple_discoveries
date_saved: 2026-05-05T03:44:22.470419+00:00
instance: kb-cron
parent: list-of-multiple-discoveries-20260505-0
summary:
1730: Stirling numbers – James Stirling (1730) and Masanobu Saka (1782). 1740s: Platinum – Antonio de Ulloa and Charles Wood (both in the 1740s). 1745: Leyden Jar – Ewald Georg von Kleist (1745) and Pieter van Musschenbroek (1745–46). 1749: Lightning rod – Benjamin Franklin (1749) and Prokop Diviš (1754) (debated: Diviš's apparatus is assumed to have been more effective than Franklin's lightning rods in 1754, but was intended for a different purpose than lightning protection). 1756: Law of conservation of matter – discovered by Mikhail Lomonosov, 1756; and independently by Antoine Lavoisier, 1778. 1773: Oxygen – Carl Wilhelm Scheele (Uppsala, 1773), Joseph Priestley (Wiltshire, 1774). The term was coined by Antoine Lavoisier (1777). Michael Sendivogius (Polish: Michał Sędziwój; 1566–1636) is claimed as an earlier discoverer of oxygen. 1783: Black-hole theory – John Michell, in a 1783 paper in The Philosophical Transactions of the Royal Society, wrote: "If the semi-diameter of a sphere of the same density as the Sun in the proportion of five hundred to one, and by supposing light to be attracted by the same force in proportion to its [mass] with other bodies, all light emitted from such a body would be made to return towards it,


id: list-of-multiple-discoveries-20260505-2
file: data/en.wikipedia.org/wiki/List_of_multiple_discoveries-2.md
category: reference
title: List of multiple discoveries
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/List_of_multiple_discoveries
date_saved: 2026-05-05T03:44:22.470419+00:00
instance: kb-cron
parent: list-of-multiple-discoveries-20260505-0
summary:
1805: In a treatise written in 1805 and published in 1866, Carl Friedrich Gauss describes an efficient algorithm to compute the discrete Fourier transform. James W. Cooley and John W. Tukey reinvented a similar algorithm in 1965. 1817: Cadmium – Friedrich Strohmeyer, K.S.L Hermann (both in 1817). 1817: Grotthuss–Draper law (aka the Principle of Photochemical Activation) – first proposed in 1817 by Theodor Grotthuss, then independently, in 1842, by John William Draper. The law states that only that light which is absorbed by a system can bring about a photochemical change. 1825: Bromine discovered by Carl Jacob Löwig; and independently, in 1826, by Antoine Jérôme Balard. 1828: Beryllium – Friedrich Wöhler, A.A.B. Bussy (1828). 1830: Non-Euclidean geometry (hyperbolic geometry) – Nikolai Ivanovich Lobachevsky (1830), János Bolyai (1832); preceded by Gauss (unpublished result) c. 1805. 1831: Electromagnetic induction was discovered by Michael Faraday in England in 1831, and independently about the same time by Joseph Henry in the U.S. 1831: Chloroform – Samuel Guthrie in the United States (July 1831), and a few months later Eugène Soubeiran (France) and Justus von Liebig (Germany), all of them using variations of the haloform reaction. Dandelin–Gräffe method, aka Lobachevsky method – an algorithm for


id: list-of-multiple-discoveries-20260505-3
file: data/en.wikipedia.org/wiki/List_of_multiple_discoveries-3.md
category: reference
title: List of multiple discoveries
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/List_of_multiple_discoveries
date_saved: 2026-05-05T03:44:22.470419+00:00
instance: kb-cron
parent: list-of-multiple-discoveries-20260505-0
summary:
E = mc2, though only Einstein provided the accepted interpretation – Henri Poincaré, 1900; Olinto De Pretto, 1903; Albert Einstein, 1905; Paul Langevin, 1906. 1902: Walter Sutton and Theodor Boveri independently proposed that the hereditary information is carried in the chromosomes. 1902: Richard Assmann and Léon Teisserenc de Bort independently discovered the stratosphere. 1904: Epinephrine synthesized independently by Friedrich Stolz and by Henry Drysdale Dakin. 1905: Brownian motion was independently explained by Albert Einstein (in one of his 1905 papers) and by Marian Smoluchowski in 1906. 1905: The Einstein Relation was revealed independently by William Sutherland in 1905, by Albert Einstein in 1905, and by Marian Smoluchowski in 1906. 1905: The chromosomal XY sex-determination system—that males have XY, and females XX, sex chromosomes—was discovered independently by Nettie Stevens, at Bryn Mawr College, and by Edmund Beecher Wilson at Columbia University. 1907: Lutetium discovered independently by French scientist Georges Urbain and by Austrian mineralogist Baron Carl Auer von Welsbach. 1907: Hilbert space representation theorem, also known as Riesz representation theorem, the mathematical justification of the Bra-ket notation in the theory of quantum mechanics – independently proved by Frigyes Riesz and Maurice René Fréchet. 1908: The Hardy–Weinberg principle is a principle of


id: list-of-multiple-discoveries-20260505-4
file: data/en.wikipedia.org/wiki/List_of_multiple_discoveries-4.md
category: reference
title: List of multiple discoveries
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/List_of_multiple_discoveries
date_saved: 2026-05-05T03:44:22.470419+00:00
instance: kb-cron
parent: list-of-multiple-discoveries-20260505-0
summary:
It was originally proved in 1934 by Aleksandr Gelfond, and again independently in 1935 by Theodor Schneider. 1934: The Penrose triangle, also known as the "tribar", is an impossible object. It was first created by the Swedish artist Oscar Reutersvärd in 1934. The mathematician Roger Penrose independently devised and popularized it in the 1950s. 1936: In computer science, the concept of the "universal computing machine" (now generally called the "Turing Machine") was proposed by Alan Turing, but also independently by Emil Post, both in 1936. Similar approaches, also aiming to cover the concept of universal computing, were introduced by S.C. Kleene, Rózsa Péter, and Alonzo Church that same year. Also in 1936, Konrad Zuse tried to build a binary electrically driven mechanical calculator with limited programability; however, Zuse's machine was never fully functional. The later Atanasoff–Berry Computer ("ABC"), designed by John Vincent Atanasoff and Clifford Berry, was the first fully electronic digital computing device; while not programmable, it pioneered important elements of modern computing, including binary arithmetic and electronic switching elements, though its special-purpose nature and lack of a changeable, stored program distinguish it from modern computers. 1938: Benford's law, also known as the Newcomb–Benford law, the law of anomalous


id: list-of-multiple-discoveries-20260505-5
file: data/en.wikipedia.org/wiki/List_of_multiple_discoveries-5.md
category: reference
title: List of multiple discoveries
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/List_of_multiple_discoveries
date_saved: 2026-05-05T03:44:22.470419+00:00
instance: kb-cron
parent: list-of-multiple-discoveries-20260505-0
summary:
1967: The affine scaling method for solving linear programming was discovered by Soviet mathematician I.I. Dikin in 1967. It went unnoticed in the West for two decades, until two groups of researchers in the U.S. reinvented it in 1985. 1968: Neutral theory of molecular evolution was introduced by a Japanese biologist, Motoo Kimura, in 1968, and independently by two American biologists, Jack Lester King and Thomas Hughes Jukes, in 1969. 1969: Thyrotropin-releasing hormone (TRH) structure was determined, and the hormone synthesized, independently by Andrew V. Schally and Roger Guillemin, who shared the 1977 Nobel Prize in Medicine. 1970: Howard Temin and David Baltimore independently discovered reverse transcriptase enzymes. The Knuth–Morris–Pratt string searching algorithm was developed by Donald Knuth and Vaughan Pratt and independently by J. H. Morris. 1971: The Cook–Levin theorem (also known as "Cook's theorem"), a result in computational complexity theory, was proven independently by Stephen Cook (1971 in the U.S.) and by Leonid Levin (1973 in the USSR). Levin was not aware of Cook's achievement because of communication difficulties between East and West during the Cold War. The other way round, Levin's work was not widely known in the West until around 1978. 1972: The Bohlen–Pierce scale, a


id: list-of-multiple-discoveries-20260505-6
file: data/en.wikipedia.org/wiki/List_of_multiple_discoveries-6.md
category: reference
title: List of multiple discoveries
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/List_of_multiple_discoveries
date_saved: 2026-05-05T03:44:22.470419+00:00
instance: kb-cron
parent: list-of-multiple-discoveries-20260505-0
summary:
== 21st century == 2001: four different authors published different implementations of a distributed hash table. The Super Kamiokande and SNOLAB collaborations, whose findings were published in 1998 and 2001 respectively, both proved that neutrinos have mass. As a result, the 2015 Nobel Prize in Physics was shared by Takaaki Kajita of Japan and Arthur B. McDonald of Canada. 1996: James Allison of MD Anderson Cancer Center at the University of Texas at Houston discovered a mechanism enabling cancer immunotherapy. In 2002 Tasuku Honjo of Kyoto University discovered another such mechanism. This outcome, which led to them sharing the 2018 Nobel Prize in Physiology or Medicine, has been described as follows: "Each independently discovered that our immune system is restrained from attacking tumors by molecules that function as 'brakes.' Releasing these brakes (or 'brake receptors') allows our body to powerfully combat cancer." 2014: Paul Erdős' conjecture about prime gaps was proved by Kevin Ford, Ben Green, Sergei Konyagin, and Terence Tao, working together, and independently by James Maynard. 2020: Half of the 2020 Nobel Prize in Physics was awarded to Reinhard Genzel and Andrea Ghez, who each have led a group of astronomers focused since the early 1990s on a


id: the-no-nonsense-guide-to-science-20260505-0
file: data/en.wikipedia.org/wiki/The_No_Nonsense_Guide_To_Science-0.md
category: reference
title: The No Nonsense Guide To Science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/The_No_Nonsense_Guide_To_Science
date_saved: 2026-05-05T03:44:23.815827+00:00
instance: kb-cron
parent: the-no-nonsense-guide-to-science-20260505-0
summary:
The No Nonsense Guide to Science is a 2006 book on Post-normal science (PNS). It was written by American born British historian and philosopher of science Jerome Ravetz. == Main == What should a young person do who aspires to make the world a better place and to make their way in science? This is how this work's ambition was summarized. Written in 2006 by one of the founding fathers of Post-normal Science - the other being Silvio Funtowicz - its 142 pages cover several themes, in part synthesizing previous works such as Scientific Knowledge and Its Social Problems, The Merger of Knowledge with Power, and Uncertainty and Quality in Science for Policy (with Funtowicz), and introduces the ideas of Post-normal Science. Topics include: The problem of science being at once 'little' and big or 'mega', embedded in institutions and corporations The fallibility of science, against a possibly 'dogmatic' teaching of the power of science The democratization of science as a necessary and realistic antidote to its hubris The opportunity of forming extended peer communities - inclusive of whistle blowers and investigative journalists as well as academics and interested stakeholders, when science is called to answer conflicted policy questions. The


id: nursing-process-20260505-0
file: data/en.wikipedia.org/wiki/Nursing_process-0.md
category: reference
title: Nursing process
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Nursing_process
date_saved: 2026-05-05T03:44:25.053005+00:00
instance: kb-cron
parent: nursing-process-20260505-0
summary:
The nursing process is a modified scientific method that is a fundamental part of nursing practices in many countries around the world. Nursing practice was first described as a four-stage nursing process by Ida Jean Orlando in 1958. It should not be confused with nursing theories or health informatics. The diagnosis phase was added later. The nursing process uses clinical judgement to strike a balance of epistemology between personal interpretation and research evidence in which critical thinking may play a part to categorize the clients issue and course of action. Nursing offers diverse patterns of knowing. Nursing knowledge has embraced pluralism since the 1970s. Evidence based practice (EBP) Evidence based practice is a process that is used in the healthcare field to used as a problem-solving approach to make clinical decisions. This is collected by reviewing, analyzing, and forming the best sources for the patient-care. EBP assist with the nursing process by providing credible information that helps nurses make the knowledgeable choice. Person-centered care The nursing process helps orchestrate the nurses' decisions with the patient’s participation needed for recovery. Nurses utilize person-centered care (PCC), which focuses on identifying and addressing a patient's unique needs and preferences. PCC aligns well with


id: observation-20260505-0
file: data/en.wikipedia.org/wiki/Observation-0.md
category: reference
title: Observation
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Observation
date_saved: 2026-05-05T03:44:26.241404+00:00
instance: kb-cron
parent: observation-20260505-0
summary:
Observation in the natural sciences refers to the active acquisition of information from a primary source. It involves the act of noticing or perceiving phenomena and gathering data based on direct engagement with the subject of study. In living organisms, observation typically occurs through the senses. In science, it often extends beyond unaided perception, involving the use of scientific instruments to detect, measure, and record data. This enables the observation of phenomena not accessible to human senses alone. Observations in science are typically categorized as either qualitative or quantitative: Qualitative observations describe characteristics that are not expressed numerically, such as color, texture, or behavior. Quantitative observations involve numerical measurements, obtained through counting or using instruments to assign values to observed phenomena. The term observation may refer both to the process of observing and to the information recorded as a result of that process. == Science == The scientific method requires observations of natural phenomena to formulate and test hypotheses. The method involves an iterative series of steps intended to generate and refine scientific knowledge: Ask a question about a phenomenon Make observations of the phenomenon Formulate a hypothesis that tentatively answers the question Predict logical, observable consequences of the hypothesis


id: observation-20260505-1
file: data/en.wikipedia.org/wiki/Observation-1.md
category: reference
title: Observation
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Observation
date_saved: 2026-05-05T03:44:26.241404+00:00
instance: kb-cron
parent: observation-20260505-0
summary:
== Biases == Human senses do not function like an impartial recording device such as a video camcorder. Perception occurs through a complex, largely unconscious process of abstraction, in which certain elements of sensory input are selected and retained, while others are discarded. This selection process depends on an internal model of the world—referred to in psychology as a schema—that is shaped by past experiences. Sensory information is interpreted and stored based on this schema. During recall, gaps in memory may be unconsciously filled with information consistent with the schema, a process known as reconstructive memory. The degree of attention given to different aspects of a perceptual experience is influenced by an individual's internal value system, which prioritizes information based on perceived importance. As a result, two individuals observing the same event may remember it differently, potentially disagreeing on factual details. This subjectivity is a known limitation of eyewitness testimony, which research has shown to be frequently unreliable. In scientific practice, rigorous methods are employed to minimize such observational biases. These include careful documentation of experimental data, distinguishing clearly between raw observations and inferred conclusions, and implementing procedures such as blind and double blind experiment designs to control for subjective


id: open-label-trial-20260505-0
file: data/en.wikipedia.org/wiki/Open-label_trial-0.md
category: reference
title: Open-label trial
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open-label_trial
date_saved: 2026-05-05T03:44:27.552355+00:00
instance: kb-cron
parent: open-label-trial-20260505-0
summary:
An open-label trial, or open trial, is a type of clinical trial in which information is not withheld from trial participants. In particular, both the researchers and participants know which treatment is being administered. This contrasts with a double-blinded trial, where information is withheld both from the researchers and the participants to reduce bias. Open-label trials may be appropriate for comparing two similar treatments to determine which is most effective, such as a comparison of different prescription anticoagulants, or possible relief from symptoms of some disorders when a placebo is given. An open-label trial may still be randomized. Open-label trials may also be uncontrolled (without a placebo group), with all participants receiving the same treatment. This would be a single-arm study design. == References ==


id: open-notebook-science-20260505-0
file: data/en.wikipedia.org/wiki/Open-notebook_science-0.md
category: reference
title: Open-notebook science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open-notebook_science
date_saved: 2026-05-05T03:44:33.661868+00:00
instance: kb-cron
parent: open-notebook-science-20260505-0
summary:
Open-notebook science is the practice of making the entire primary record of a research project publicly available online as it is recorded. This involves placing the personal, or laboratory, notebook of the researcher online along with all raw and processed data, and any associated material, as this material is generated. The approach may be summed up by the slogan 'no insider information'. It is the logical extreme of transparent approaches to research and explicitly includes the making available of failed, less significant, and otherwise unpublished experiments; so called 'dark data'. The practice of open notebook science, although not the norm in the academic community, has gained significant recent attention in the research and general media as part of a general trend towards more open approaches in research practice and publishing. Open notebook science can therefore be described as part of a wider open science movement that includes the advocacy and adoption of open access publication, open data, crowdsourcing data, and citizen science. It is inspired in part by the success of open-source software and draws on many of its ideas. == History == The term "open-notebook science" was first used in 2006 in a blog post by Jean-Claude Bradley, an


id: open-notebook-science-20260505-1
file: data/en.wikipedia.org/wiki/Open-notebook_science-1.md
category: reference
title: Open-notebook science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open-notebook_science
date_saved: 2026-05-05T03:44:33.661868+00:00
instance: kb-cron
parent: open-notebook-science-20260505-0
summary:
== Drawbacks == The arguments against adopting open notebook science fall mainly into three categories which have differing importance in different fields of science. The primary concern, expressed particularly by biological and medical scientists is that of 'data theft' or 'being scooped'. While the degree to which research groups steal or adapt the results of others remains a subject of debate it is certainly the case that the fear of not being first to publish drives much behavior, particularly in some fields. This is related to the focus in these fields on the published peer-reviewed paper as being the main metric of career success. The second argument advanced against open notebook science is that it constitutes prior publication, thus making it impossible to patent and difficult to publish the results in the traditional peer reviewed literature. With respect to patents, publication on the web is clearly classified as disclosure. Therefore, while there may be arguments over the value of patents, and approaches that get around this problem, it is clear that open notebook science is not appropriate for research for which patent protection is an expected and desired outcome. With respect to publication in the peer-reviewed literature the case is


id: operationalization-20260505-0
file: data/en.wikipedia.org/wiki/Operationalization-0.md
category: reference
title: Operationalization
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Operationalization
date_saved: 2026-05-05T03:44:34.920226+00:00
instance: kb-cron
parent: operationalization-20260505-0
summary:
In research design, especially in psychology, social sciences, life sciences and physics, operationalization (or operationalisation) is a process of defining the measurement of a phenomenon which is not directly measurable, though its existence is inferred from other phenomena. Operationalization thus defines a fuzzy concept so as to make it clearly distinguishable, measurable, and understandable by empirical observation. In a broader sense, it defines the extension of a concept—describing what is and is not an instance of that concept. For example, in medicine, the phenomenon of health might be operationalized by one or more indicators like body mass index or tobacco smoking. As another example, in visual processing the presence of a certain object in the environment could be inferred by measuring specific features of the light it reflects. In these examples, the phenomena are difficult to directly observe and measure because they are general/abstract (as in the example of health) or they are latent (as in the example of the object). Operationalization helps infer the existence, and some elements of the extension, of the phenomena of interest by means of some observable and measurable effects they have. Sometimes multiple or competing alternative operationalizations for the same phenomenon are available. Repeating


id: operationalization-20260505-1
file: data/en.wikipedia.org/wiki/Operationalization-1.md
category: reference
title: Operationalization
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Operationalization
date_saved: 2026-05-05T03:44:34.920226+00:00
instance: kb-cron
parent: operationalization-20260505-0
summary:
=== Anger example === For example, a researcher may wish to measure the concept "anger." Its presence, and the depth of the emotion, cannot be directly measured by an outside observer because anger is intangible. Rather, other measures are used by outside observers, such as facial expression, choice of vocabulary, loudness and tone of voice. If a researcher wants to measure the depth of "anger" in various persons, the most direct operation would be to ask them a question, such as "are you angry", or "how angry are you?". This operation is problematic, however, because it depends upon the definition of the individual. Some people might be subjected to a mild annoyance, and become slightly angry, but describe themselves as "extremely angry," whereas others might be subjected to a severe provocation, and become very angry, but describe themselves as "slightly angry." In addition, in many circumstances it is impractical to ask subjects whether they are angry. Since one of the measures of anger is loudness, the researcher can operationalize the concept of anger by measuring how loudly subjects speak compared to their normal tones. However, this must assume that loudness is a uniform measure. Some might respond verbally while others


id: outline-of-scientific-method-20260505-0
file: data/en.wikipedia.org/wiki/Outline_of_scientific_method-0.md
category: reference
title: Outline of scientific method
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Outline_of_scientific_method
date_saved: 2026-05-05T03:44:36.226294+00:00
instance: kb-cron
parent: outline-of-scientific-method-20260505-0
summary:
The following outline is provided as an overview of and topical guide to the scientific method: Scientific method – body of techniques for investigating phenomena and acquiring new knowledge, as well as for correcting and integrating previous knowledge. It is based on observable, empirical, reproducible, measurable evidence, and subject to the laws of reasoning. == Nature of scientific method == Scientific method Science Philosophy of science Sociology of knowledge Process of science Knowledge == Elements of scientific method == Research Basic research === Observation === Observation Scientific method Causation Investigation Measurement (Outline of metrology and measurement) === Hypothesis === Hypothesis pro:Karl Popper Falsifiability con:Paul Feyerabend Statistical hypothesis testing === Experiment === Experiment Laboratory Laboratory techniques Design of experiments Scientific control Natural experiment Observational study Field experiment Self-experimentation Self-experimentation in medicine Placebo effect === Theory === Scientific theory ==== Prediction ==== Prediction Bayesian inference – subjective use of statistical reasoning Deductive reasoning Retrodiction === Evaluation by scientific community === Peer review Medical peer review == Scientific method concepts == === Empirical methods === Empirical methods Empiricism Robert Grosseteste Peter Parker Empirical validation Operationalization === Use of statistics === Uncomfortable science — Inference from a limited sample of data Exploratory data analysis


id: pasteur-s-quadrant-20260505-0
file: data/en.wikipedia.org/wiki/Pasteur's_quadrant-0.md
category: reference
title: Pasteur's quadrant
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Pasteur's_quadrant
date_saved: 2026-05-05T03:44:37.455516+00:00
instance: kb-cron
parent: pasteur-s-quadrant-20260505-0
summary:
Pasteur's quadrant is a classification of scientific research projects that seek fundamental understanding of scientific problems, while also having immediate use for society. Louis Pasteur's research is thought to exemplify this type of method, which bridges the gap between "basic" and "applied" research. The term was introduced by Donald E. Stokes in his book, Pasteur's Quadrant. == Other quadrants == As shown in the following table, scientific research can be classified by whether it advances human knowledge by seeking a fundamental understanding of nature, or whether it is primarily motivated by the need to solve immediate problems. The result is three distinct classes of research: Pure basic research, exemplified by the work of Niels Bohr, early 20th century atomic physicist. Pure applied research, exemplified by the work of Thomas Edison, inventor. Use-inspired basic research, described here as "Pasteur's Quadrant". == Usage == Pasteur's quadrant is useful in distinguishing various perspectives within science, engineering and technology. For example, Daniel A. Vallero and Trevor M. Letcher in their book Unraveling Environmental Disasters applied the device to disaster preparedness and response. University science programs are concerned with knowledge-building, whereas engineering programs at the same university will apply existing and emerging knowledge to address


id: scholarly-peer-review-20260505-0
file: data/en.wikipedia.org/wiki/Scholarly_peer_review-0.md
category: reference
title: Scholarly peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scholarly_peer_review
date_saved: 2026-05-05T03:44:38.682666+00:00
instance: kb-cron
parent: scholarly-peer-review-20260505-0
summary:
Scholarly peer review or academic peer review (also known as refereeing) is the process of having a draft version of a researcher's methods and findings reviewed (usually anonymously) by experts (or "peers") in the same field. Peer review is widely used for helping the academic publisher (i.e., the editor-in-chief, the editorial board, or the program committee) decide whether the work should be accepted, considered acceptable with revisions, or rejected for official publication in an academic journal, a monograph, or in the proceedings of an academic conference. If the identities of authors are not revealed to each other, the procedure is called dual-anonymous peer review. Academic peer review requires a community of experts in a given (and often narrowly defined) academic field, who are qualified and able to perform reasonably impartial review. Impartial review, especially of work in less narrowly defined or inter-disciplinary fields, may be difficult to accomplish, and the significance (good or bad) of an idea may never be widely appreciated among its contemporaries. Peer review is generally considered necessary to academic quality and is used in most major scholarly journals. However, peer review does not prevent publication of invalid research, and as experimentally controlled studies of this process


id: scholarly-peer-review-20260505-1
file: data/en.wikipedia.org/wiki/Scholarly_peer_review-1.md
category: reference
title: Scholarly peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scholarly_peer_review
date_saved: 2026-05-05T03:44:38.682666+00:00
instance: kb-cron
parent: scholarly-peer-review-20260505-0
summary:
Workload. A small group of editors/assessors cannot devote sufficient time to each of the many articles submitted to many journals. Miscellany of ideas. Were the editor/assessor to judge all submitted material themselves, approved material would solely reflect their opinion. Limited expertise. An editor/assessor cannot be expected to be sufficiently expert in all areas covered by a single journal or funding agency to adequately judge all submitted material. Reviewers are often anonymous and independent. However, some reviewers may choose to waive their anonymity, and in other limited circumstances, such as the examination of a formal complaint against the referee, or a court order, the reviewer's identity may have to be disclosed. Anonymity may be unilateral or reciprocal (single- or double-blinded reviewing). Since reviewers are normally selected from experts in the fields discussed in the article, the process of peer review helps to keep some invalid or unsubstantiated claims out of the body of published research and knowledge. Scholars will read published articles outside their limited area of detailed expertise, and then rely, to some degree, on the peer-review process to have provided reliable and credible research that they can build upon for subsequent or related research. Significant scandal ensues when an


id: scholarly-peer-review-20260505-2
file: data/en.wikipedia.org/wiki/Scholarly_peer_review-2.md
category: reference
title: Scholarly peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scholarly_peer_review
date_saved: 2026-05-05T03:44:38.682666+00:00
instance: kb-cron
parent: scholarly-peer-review-20260505-0
summary:
to unconditionally accept the manuscript or the proposal, to accept it in the event that its authors improve it in certain ways to reject it, but encourage revision and invite re-submission to reject it outright. During this process, the role of the referees is advisory. The editor(s) is typically under no obligation to accept the opinions of the referees. However, the editor will most often follow the advice of the referees. Furthermore, the referees in scientific publication do not act as a group, do not communicate with each other, and typically are not aware of each other's identities or evaluations. Proponents argue that if the reviewers of a paper are unknown to each other, the editor(s) can more easily verify the objectivity of the reviews. There is usually no requirement that the referees achieve consensus, with the decision instead often made by the editor(s) based on her best judgement of the arguments. In situations where multiple referees disagree substantially about the quality of a work, there are a number of strategies for reaching a decision. The paper may be rejected outright, or the editor may choose which reviewer's point the authors should address. When a publisher receives very positive and


id: scholarly-peer-review-20260505-3
file: data/en.wikipedia.org/wiki/Scholarly_peer_review-3.md
category: reference
title: Scholarly peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scholarly_peer_review
date_saved: 2026-05-05T03:44:38.682666+00:00
instance: kb-cron
parent: scholarly-peer-review-20260505-0
summary:
== Recruiting referees == At a journal or book publisher, the task of picking reviewers typically falls to an editor. When a manuscript arrives, an editor solicits reviews from scholars or other experts who may or may not have already expressed a willingness to referee for that journal or book division. Granting agencies typically recruit a panel or committee of reviewers in advance of the arrival of applications. Referees are supposed to inform the editor of any conflict of interests that might arise. Journals or individual editors may invite a manuscript's authors to name people whom they consider qualified to referee their work. For some journals this is a requirement of submission. Authors are sometimes also given the opportunity to name natural candidates who should be disqualified, in which case they may be asked to provide justification (typically expressed in terms of conflict of interest). Editors solicit author input in selecting referees because academic writing typically is very specialized. Editors often oversee many specialties, and can not be experts in all of them. But after an editor selects referees from the pool of candidates, the editor typically is obliged not to disclose the referees' identities to the authors, and in


id: scholarly-peer-review-20260505-4
file: data/en.wikipedia.org/wiki/Scholarly_peer_review-4.md
category: reference
title: Scholarly peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scholarly_peer_review
date_saved: 2026-05-05T03:44:38.682666+00:00
instance: kb-cron
parent: scholarly-peer-review-20260505-0
summary:
=== Internal and external peer review === External peer review is the ordinary process of peer review for scholarly journals. The first evidence of formal external peer reviews dates back to 1752, and it became widely used and more standardized after World War II. In this process, scholars who are not beholden to the journal or the author review submitted articles, and in doing so, restrain academic and scientific journals from using solely their own commercial interests as a guide on what to publish. Nature began requiring external peer review in 1973, and other journals quickly followed suit. Internal peer review is having a colleague in the same department or academic institution read over a paper before submitting it to a journal for external peer review. For example, economists in the same government agency may review their peers' papers, as a type of peer professional accountability. People such as Nobel laureate Dan Shechtman and organizations such as the US National Institute of Standards and Technology use internal peer review because they believe that it gives them an opportunity to improve papers before submitting them to a journal. Internal peer review is also recommended for authors with weak writing skills. Unlike


id: scholarly-peer-review-20260505-5
file: data/en.wikipedia.org/wiki/Scholarly_peer_review-5.md
category: reference
title: Scholarly peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scholarly_peer_review
date_saved: 2026-05-05T03:44:38.682666+00:00
instance: kb-cron
parent: scholarly-peer-review-20260505-0
summary:
=== Transparent peer review === In transparent peer review the documents relating to the publication of the paper are published alongside the paper. This includes the initial report made by the peer reviewer, the authors' response to it, and the acceptance letter by the editor. The names of the reviewers may still be kept anonymous even in transparent peer review. === Pre- and post-publication peer review === The process of peer review is not restricted to the publication process managed by academic journals. In particular, some forms of peer review can occur before an article is submitted to a journal and/or after it is published by the journal. ==== Pre-publication peer review ==== Manuscripts are typically reviewed by colleagues before submission, and if the manuscript is uploaded to preprint servers, such as ArXiv, BioRxiv or SSRN, researchers can read and comment on the manuscript. The practice to upload to preprint servers, and the activity of discussion heavily depend on the field, and it allows an open pre-publication peer review. The advantage of this method is speed and transparency of the review process. Anyone can give feedback, typically in form of comments, and typically not anonymously. These comments are also public,


id: scholarly-peer-review-20260505-6
file: data/en.wikipedia.org/wiki/Scholarly_peer_review-6.md
category: reference
title: Scholarly peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scholarly_peer_review
date_saved: 2026-05-05T03:44:38.682666+00:00
instance: kb-cron
parent: scholarly-peer-review-20260505-0
summary:
Result-blind peer review or results blind peer review, first proposed 1966: Reviewers receive an edited version of the submitted paper which omits the results and conclusion section. In a two-stage version, a second round of reviews or editorial judgment is based on the full paper version, which was first proposed in 1977. Conclusion-blind review, proposed by Robin Hanson in 2007 extends this further asking all authors to submit a positive and a negative version, and only after the journal has accepted the article authors reveal which is the real version. Pre-accepted articles or outcome-unbiased journals or advance publication review or registered reports or prior to results submission or early acceptance extends study pre-registration to the point that journals accepted or reject papers based on the version of the paper written before the results or conclusions have been made (an enlarged study protocol), but instead describes the theoretical justification, experimental design, and statistical analysis. Only once the proposed hypothesis and methodology have been accepted by reviewers, the authors would collect the data or analyze previously collected data. A limited variant of a pre-accepted article was The Lancet's study protocol review from 1997 to 2015 reviewed and published randomized trial protocols with


id: scholarly-peer-review-20260505-7
file: data/en.wikipedia.org/wiki/Scholarly_peer_review-7.md
category: reference
title: Scholarly peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scholarly_peer_review
date_saved: 2026-05-05T03:44:38.682666+00:00
instance: kb-cron
parent: scholarly-peer-review-20260505-0
summary:
Scholarly peer review has been subject to several criticisms, and various proposals for reforming the system have been suggested over the years. Many studies have emphasized the problems inherent to the process of peer review. Moreover, Ragone et al. have shown that there is a low correlation between peer review outcomes and the future impact measured by citations. Various biomedical editors in particular have expressed criticism of peer review. A Cochrane review found little empirical evidence that peer review ensures quality in biomedical research, while a second systematic review and meta-analysis found a need for evidence-based peer review in biomedicine given the paucity of assessment of the interventions designed to improve the process. To an outsider, the anonymous, pre-publication peer review process is opaque. Certain journals are accused of not carrying out stringent peer review in order to more easily expand their customer base, particularly in journals where authors pay a fee before publication. Richard Smith, MD, former editor of the British Medical Journal, has claimed that peer review is "ineffective, largely a lottery, anti-innovatory, slow, expensive, wasteful of scientific time, inefficient, easily abused, prone to bias, unable to detect fraud and irrelevant; Several studies have shown that peer review


id: scholarly-peer-review-20260505-8
file: data/en.wikipedia.org/wiki/Scholarly_peer_review-8.md
category: reference
title: Scholarly peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scholarly_peer_review
date_saved: 2026-05-05T03:44:38.682666+00:00
instance: kb-cron
parent: scholarly-peer-review-20260505-0
summary:
=== Peer review and trust === Researchers have peer-reviewed manuscripts prior to publishing them in a variety of ways since the 18th century. The main goal of this practice is to improve the relevance and accuracy of scientific discussions. Even though experts often criticize peer review for a number of reasons, the process is still often considered the "gold standard" of science. Occasionally however, peer review approves studies that are later found to be wrong and rarely deceptive or fraudulent results are discovered prior to publication. Thus, there seems to be an element of discord between the ideology behind and the practice of peer review. By failing to effectively communicate that peer review is imperfect, the message conveyed to the wider public is that studies published in peer-reviewed journals are "true" and that peer review protects the literature from flawed science. A number of well-established criticisms exist of many elements of peer review. In the following we describe cases of the wider impact inappropriate peer review can have on public understanding of scientific literature. Multiple examples across several areas of science find that scientists elevated the importance of peer review for research that was questionable or corrupted. For example, climate


id: scholarly-peer-review-20260505-9
file: data/en.wikipedia.org/wiki/Scholarly_peer_review-9.md
category: reference
title: Scholarly peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scholarly_peer_review
date_saved: 2026-05-05T03:44:38.682666+00:00
instance: kb-cron
parent: scholarly-peer-review-20260505-0
summary:
The belief that scholars are incapable of evaluating the quality of work on their own, that they are in need of a gatekeeper to inform them of what is good and what is not. The belief that scholars need a "guardian" to make sure they are doing good work. Others argue that authors most of all have a vested interest in the quality of a particular piece of work. Only the authors could have, as Feynman (1974) puts it, the "extra type of integrity that is beyond not lying, but bending over backwards to show how you're maybe wrong, that you ought to have when acting as a scientist." If anything, the current peer review process and academic system could penalize, or at least fail to incentivize, such integrity. Instead, the credibility conferred by the "peer-reviewed" label could diminish what Feynman calls the culture of doubt necessary for science to operate a self-correcting, truth-seeking process. The effects of this can be seen in the ongoing replication crisis, hoaxes, and widespread outrage over the inefficacy of the current system. It's common to think that more oversight is the answer, as peer reviewers are not at all lacking in skepticism. But the


id: scholarly-peer-review-20260505-10
file: data/en.wikipedia.org/wiki/Scholarly_peer_review-10.md
category: reference
title: Scholarly peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scholarly_peer_review
date_saved: 2026-05-05T03:44:38.682666+00:00
instance: kb-cron
parent: scholarly-peer-review-20260505-0
summary:
=== Allegations of bias and suppression === The interposition of editors and reviewers between authors and readers may enable the intermediators to act as gatekeepers. Some sociologists of science argue that peer review makes the ability to publish susceptible to control by elites and to personal jealousy. The peer review process may sometimes impede progress and may be biased against novelty. A linguistic analysis of review reports suggests that reviewers focus on rejecting the applications by searching for weak points, and not on finding the high-risk/high-gain groundbreaking ideas that may be in the proposal. Reviewers tend to be especially critical of conclusions that contradict their own views, and lenient towards those that match them. At the same time, established scientists are more likely than others to be sought out as referees, particularly by high-prestige journals/publishers. As a result, ideas that harmonize with the established experts' are more likely to see print and to appear in premier journals than are iconoclastic or revolutionary ones. This accords with Thomas Kuhn's well-known observations regarding scientific revolutions. A theoretical model has been established whose simulations imply that peer review and over-competitive research funding foster mainstream opinion to monopoly. Criticisms of traditional anonymous peer review


id: scholarly-peer-review-20260505-11
file: data/en.wikipedia.org/wiki/Scholarly_peer_review-11.md
category: reference
title: Scholarly peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scholarly_peer_review
date_saved: 2026-05-05T03:44:38.682666+00:00
instance: kb-cron
parent: scholarly-peer-review-20260505-0
summary:
==== Plagiarism ==== Reviewers generally lack access to raw data, but do see the full text of the manuscript, and are typically familiar with recent publications in the area. Thus, they are in a better position to detect plagiarism of prose than fraudulent data. A few cases of such textual plagiarism by historians, for instance, have been widely publicized. On the scientific side, a poll of 3,247 scientists funded by the U.S. National Institutes of Health found 0.3% admitted faking data and 1.4% admitted plagiarism. Additionally, 4.7% of the same poll admitted to self-plagiarism or autoplagiarism, in which an author republishes the same material, data, or text, without citing their earlier work. Self-plagiarisms are less likely to be detected in double-blinded peer reviews. ==== Examples ==== "Perhaps the most widely recognized failure of peer review is its inability to ensure the identification of high-quality work. The list of important scientific papers that were rejected by some peer-reviewed journals goes back at least as far as the editor of Philosophical Transaction's 1796 rejection of Edward Jenner's report of the first vaccination against smallpox." The Soon and Baliunas controversy involved the publication in 2003 of a review study written by aerospace engineer


id: personoid-20260505-0
file: data/en.wikipedia.org/wiki/Personoid-0.md
category: reference
title: Personoid
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Personoid
date_saved: 2026-05-05T03:44:39.870655+00:00
instance: kb-cron
parent: personoid-20260505-0
summary:
Personoid is the concept coined by Stanisław Lem, a Polish science-fiction writer, in Non Serviam, from his book A Perfect Vacuum (1971). His personoids are an abstraction of functions of human mind and they live in computers; they do not need any human-like physical body. In cognitive and software modeling, personoid is a research approach to the development of intelligent autonomous agents. In frame of the IPK (Information, Preferences, Knowledge) architecture, it is a framework of abstract intelligent agent with a cognitive and structural intelligence. It can be seen as an essence of high intelligent entities. From the philosophical and systemics perspectives, personoid societies can also be seen as the carriers of a culture. According to N. Gessler, the personoids study can be a base for the research on artificial culture and culture evolution. == Personoids on TV and cinema == Welt am Draht (1973) The Thirteenth Floor (1999) == See also == Android Humanoid Intelligence Artificial Intelligence Culture Computer Science Cognitive Science Anticipatory science Memetics == References == Stanisław Lem's book Próżnia Doskonała (1971). The collection of book reviews of nonexistent books. Translated into English by Michael Kandel as A Perfect Vacuum (1983). Personetics. Personoids Organizations Framework: An Approach


id: pilot-experiment-20260505-0
file: data/en.wikipedia.org/wiki/Pilot_experiment-0.md
category: reference
title: Pilot experiment
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Pilot_experiment
date_saved: 2026-05-05T03:44:41.187055+00:00
instance: kb-cron
parent: pilot-experiment-20260505-0
summary:
A pilot experiment, pilot study, pilot test or pilot project is a small-scale preliminary study conducted to evaluate feasibility, duration, cost, adverse events, and improve upon the study design prior to performance of a full-scale research project. == Implementation == Pilot experiments are frequently carried out before large-scale quantitative research, in an attempt to avoid time and money being used on an inadequately designed project. A pilot study is usually carried out on members of the relevant population. A pilot study is used to formulate the design of the full-scale experiment which then can be adjusted. The pilot study is potentially a critical insight to clinical trial design, recruitment and sample size of participants, treatment testing, and statistical analysis to improve the power of testing the hypothesis of the study. Analysis from the pilot experiment can be added to the full-scale (and more expensive) experiment to improve the chances of a clear outcome. == Applications == In sociology, pilot studies can be referred to as small-scale studies that will help identify design issues before the main research is done. Although pilot experiments have a well-established tradition, their usefulness as a strategy for change has been questioned, at least in the


id: positivism-20260505-0
file: data/en.wikipedia.org/wiki/Positivism-0.md
category: reference
title: Positivism
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Positivism
date_saved: 2026-05-05T03:44:42.580777+00:00
instance: kb-cron
parent: positivism-20260505-0
summary:
Positivism is a philosophical school that holds that all genuine knowledge is either true by definition or positive – the latter meaning a posteriori facts derived by reason and logic from sensory experience. Other ways of knowing, such as intuition, introspection, or religious faith, are rejected or considered meaningless. Although the positivist approach has been a recurrent theme in the history of Western thought, modern positivism was first articulated in the early 19th century by Auguste Comte. His school of sociological positivism holds that society, like the physical world, operates according to scientific laws. After Comte, positivist schools arose in logic, psychology, economics, historiography, and other fields of thought. Generally, positivists attempted to introduce scientific methods to their respective fields. Since the turn of the 20th century, positivism, although still popular, has declined under criticism within the social sciences by antipositivists and critical theorists, among others, for its alleged scientism, reductionism, overgeneralizations, and methodological limitations. Positivism also exerted an unusual influence on Kardecism. == Etymology == The English noun positivism in this meaning was imported in the 19th century from the French word positivisme, derived from positif in its philosophical sense of 'imposed on the mind by experience'. The corresponding


id: positivism-20260505-1
file: data/en.wikipedia.org/wiki/Positivism-1.md
category: reference
title: Positivism
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Positivism
date_saved: 2026-05-05T03:44:42.580777+00:00
instance: kb-cron
parent: positivism-20260505-0
summary:
The most important thing to determine was the natural order in which the sciences stand—not how they can be made to stand, but how they must stand, irrespective of the wishes of any one. ... This Comte accomplished by taking as the criterion of the position of each the degree of what he called "positivity," which is simply the degree to which the phenomena can be exactly determined. This, as may be readily seen, is also a measure of their relative complexity, since the exactness of a science is in inverse proportion to its complexity. The degree of exactness or positivity is, moreover, that to which it can be subjected to mathematical demonstration, and therefore mathematics, which is not itself a concrete science, is the general gauge by which the position of every science is to be determined. Generalizing thus, Comte found that there were five great groups of phenomena of equal classificatory value but of successively decreasing positivity. To these he gave the names astronomy, physics, chemistry, biology, and sociology. Comte offered an account of social evolution, proposing that society undergoes three phases in its quest for the truth according to a general "law of three stages". Comte intended


id: positivism-20260505-2
file: data/en.wikipedia.org/wiki/Positivism-2.md
category: reference
title: Positivism
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Positivism
date_saved: 2026-05-05T03:44:42.580777+00:00
instance: kb-cron
parent: positivism-20260505-0
summary:
Comte's fame today owes in part to Emile Littré, who founded The Positivist Review in 1867. As an approach to the philosophy of history, positivism was appropriated by historians such as Hippolyte Taine. Many of Comte's writings were translated into English by the Whig writer, Harriet Martineau, regarded by some as the first female sociologist. Debates continue to rage as to how much Comte appropriated from the work of his mentor, Saint-Simon. He was nevertheless influential: Brazilian thinkers turned to Comte's ideas about training a scientific elite in order to flourish in the industrialization process. Brazil's national motto, Ordem e Progresso ("Order and Progress") was taken from the positivism motto, "Love as principle, order as the basis, progress as the goal", which was also influential in Poland. In later life, Comte developed a 'religion of humanity' for positivist societies in order to fulfil the cohesive function once held by traditional worship. In 1849, he proposed a calendar reform called the 'positivist calendar'. For close associate John Stuart Mill, it was possible to distinguish between a "good Comte" (the author of the Course in Positive Philosophy) and a "bad Comte" (the author of the secular-religious system). The system was unsuccessful but


id: positivism-20260505-3
file: data/en.wikipedia.org/wiki/Positivism-3.md
category: reference
title: Positivism
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Positivism
date_saved: 2026-05-05T03:44:42.580777+00:00
instance: kb-cron
parent: positivism-20260505-0
summary:
=== Historical positivism === In historiography, historical or documentary positivism is the belief that historians should pursue the objective truth of the past by allowing historical sources to "speak for themselves", without additional interpretation. In the words of the French historian Fustel de Coulanges, as a positivist, "It is not I who am speaking, but history itself". The heavy emphasis placed by historical positivists on documentary sources led to the development of methods of source criticism, which seek to expunge bias and uncover original sources in their pristine state. The origin of the historical positivist school is particularly associated with the 19th-century German historian Leopold von Ranke, who argued that the historian should seek to describe historical truth "wie es eigentlich gewesen ist" ("as it actually was")—though subsequent historians of the concept, such as Georg Iggers, have argued that its development owed more to Ranke's followers than Ranke himself. Historical positivism was critiqued in the 20th century by historians and philosophers of history from various schools of thought, including Ernst Kantorowicz in Weimar Germany—who argued that "positivism ... faces the danger of becoming Romantic when it maintains that it is possible to find the Blue Flower of truth without preconceptions"—and


id: positivism-20260505-4
file: data/en.wikipedia.org/wiki/Positivism-4.md
category: reference
title: Positivism
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Positivism
date_saved: 2026-05-05T03:44:42.580777+00:00
instance: kb-cron
parent: positivism-20260505-0
summary:
== Criticism == Historically, positivism has been criticized for its reductionism, i.e., for contending that all "processes are reducible to physiological, physical or chemical events," "social processes are reducible to relationships between and actions of individuals," and that "biological organisms are reducible to physical systems." The consideration that laws in physics may not be absolute but relative, and, if so, this might be even more true of social sciences, was stated, in different terms, by G. B. Vico in 1725. Vico, in contrast to the positivist movement, asserted the superiority of the science of the human mind (the humanities, in other words), on the grounds that natural sciences tell us nothing about the inward aspects of things. Wilhelm Dilthey fought strenuously against the assumption that only explanations derived from science are valid. He reprised Vico's argument that scientific explanations do not reach the inner nature of phenomena and it is humanistic knowledge that gives us insight into thoughts, feelings and desires. Dilthey was in part influenced by the historism of Leopold von Ranke (1795–1886). The contesting views over positivism are reflected both in older debates (see the Positivism dispute) and current ones over the proper role of science in the


id: positivism-20260505-5
file: data/en.wikipedia.org/wiki/Positivism-5.md
category: reference
title: Positivism
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Positivism
date_saved: 2026-05-05T03:44:42.580777+00:00
instance: kb-cron
parent: positivism-20260505-0
summary:
Although Karl Marx's theory of historical materialism drew upon positivism, the Marxist tradition would also go on to influence the development of antipositivist critical theory. Critical theorist Jürgen Habermas critiqued pure instrumental rationality (in its relation to the cultural "rationalisation" of the modern West) as a form of scientism, or science "as ideology". He argued that positivism may be espoused by "technocrats" who believe in the inevitability of social progress through science and technology. New movements, such as critical realism, have emerged in order to reconcile postpositivist aims with various so-called 'postmodern' perspectives on the social acquisition of knowledge. Max Horkheimer criticized the classic formulation of positivism on two grounds. First, he claimed that it falsely represented human social action. The first criticism argued that positivism systematically failed to appreciate the extent to which the so-called social facts it yielded did not exist 'out there', in the objective world, but were themselves a product of socially and historically mediated human consciousness. Positivism ignored the role of the 'observer' in the constitution of social reality and thereby failed to consider the historical and social conditions affecting the representation of social ideas. Positivism falsely represented the object of study by reifying social


id: positivism-20260505-6
file: data/en.wikipedia.org/wiki/Positivism-6.md
category: reference
title: Positivism
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Positivism
date_saved: 2026-05-05T03:44:42.580777+00:00
instance: kb-cron
parent: positivism-20260505-0
summary:
== Positivism today == Echoes of the "positivist" and "antipositivist" debate persist today, though this conflict is hard to define. Authors writing in different epistemological perspectives do not phrase their disagreements in the same terms and rarely actually speak directly to each other. To complicate the issues further, few practising scholars explicitly state their epistemological commitments, and their epistemological position thus has to be guessed from other sources such as choice of methodology or theory. However, no perfect correspondence between these categories exists, and many scholars critiqued as "positivists" are actually postpositivists. One scholar has described this debate in terms of the social construction of the "other", with each side defining the other by what it is not rather than what it is, and then proceeding to attribute far greater homogeneity to their opponents than actually exists. Thus, it is better to understand this not as a debate but as two different arguments: the "antipositivist" articulation of a social meta-theory which includes a philosophical critique of scientism, and "positivist" development of a scientific research methodology for sociology with accompanying critiques of the reliability and validity of work that they see as violating such standards. Strategic positivism aims to bridge these


id: positivism-20260505-7
file: data/en.wikipedia.org/wiki/Positivism-7.md
category: reference
title: Positivism
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Positivism
date_saved: 2026-05-05T03:44:42.580777+00:00
instance: kb-cron
parent: positivism-20260505-0
summary:
Any sound scientific theory, whether of time or of any other concept, should in my opinion be based on the most workable philosophy of science: the positivist approach put forward by Karl Popper and others. According to this way of thinking, a scientific theory is a mathematical model that describes and codifies the observations we make. A good theory will describe a large range of phenomena on the basis of a few simple postulates and will make definite predictions that can be tested. ... If one takes the positivist position, as I do, one cannot say what time actually is. All one can do is describe what has been found to be a very good mathematical model for time and say what predictions it makes. == See also == Cliodynamics Científico Charvaka Determinism Gödel's incompleteness theorems London Positivist Society Nature versus nurture Physics envy Scientific politics Sociological naturalism The New Paul and Virginia Vladimir Solovyov == Notes == == References == Amory, Frederic. "Euclides da Cunha and Brazilian Positivism". Luso-Brazilian Review. 36 (1 (Summer 1999)): 87–94. Armenteros, Carolina. 2017. "The Counterrevolutionary Comte: Theorist of the Two Powers and Enthusiastic Medievalist." In The Anthem Companion to Auguste Comte, edited by Andrew


id: post-normal-science-20260505-0
file: data/en.wikipedia.org/wiki/Post-normal_science-0.md
category: reference
title: Post-normal science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Post-normal_science
date_saved: 2026-05-05T03:44:43.904474+00:00
instance: kb-cron
parent: post-normal-science-20260505-0
summary:
Post-normal science (PNS) was developed in the 1990s by Silvio Funtowicz and Jerome R. Ravetz. It is a problem-solving strategy appropriate when "facts [are] uncertain, values in dispute, stakes high and decisions urgent", conditions often present in policy-relevant research. In those situations, PNS recommends suspending temporarily the traditional scientific ideal of truth, concentrating on quality as assessed by internal and extended peer communities. PNS can be considered as complementing the styles of analysis based on risk and cost-benefit analysis prevailing at that time and integrating concepts of a new critical science developed in previous works by the same authors. PNS is not a new scientific method following Aristotle and Bacon, a new paradigm in the Kuhnian sense, or an attempt to reach a new 'normal'. It is instead, a set of insights to guide actionable and robust knowledge production for policy decision making and action in challenges like pandemics, ecosystems collapse, biodiversity loss and, in general, sustainability transitions. == Context == According to its proponents Silvio Funtowicz and Jerome R. Ravetz, the name "post-normal science" echoes the seminal work on modern science by Thomas Kuhn. For Carrozza PNS can be "framed in terms of a call for the 'democratization of


id: post-normal-science-20260505-1
file: data/en.wikipedia.org/wiki/Post-normal_science-1.md
category: reference
title: Post-normal science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Post-normal_science
date_saved: 2026-05-05T03:44:43.904474+00:00
instance: kb-cron
parent: post-normal-science-20260505-0
summary:
== Applications == Beside its dominating influence in the literature on 'futures', PNS is considered to have influenced the ecological 'conservation versus preservation debate', especially via its reading by American pragmatist Bryan G. Norton. According to Jozef Keulartz the PNS concept of "extended peer community" influenced how Norton's developed his 'convergence hypothesis'. The hypothesis posits that ecologists of different orientation will converge once they start thinking 'as a mountain', or as a planet. For Norton this will be achieved via deliberative democracy, which will pragmatically overcome the black and white divide between conservationists and preservationists. More recently it has been argued that conservation science, embedded as it is in a multi-layered governance structures of policy-makers, practitioners, and stakeholders, is itself an 'extended peer community', and as a result conservation has always been 'post-normal'. Other authors attribute to PNS the role of having stimulated the take up of transdisciplinary methodological frameworks, reliant on the social constructivist perspective embedded in PNS. Post-normal science is intended as applicable to most instances where the use of evidence is contested due to different norms and values. Typical instances are in the use of evidence based policy and in evaluation. As summarized in a recent work


id: postnormal-times-20260505-0
file: data/en.wikipedia.org/wiki/Postnormal_times-0.md
category: reference
title: Postnormal times
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Postnormal_times
date_saved: 2026-05-05T03:44:45.401782+00:00
instance: kb-cron
parent: postnormal-times-20260505-0
summary:
Postnormal times (PNT) is a concept developed by Ziauddin Sardar as a development of post-normal science. Sardar describes the present as "postnormal times", "in an in-between period where old orthodoxies are dying, new ones have yet to be born, and very few things seem to make sense." == Context == In support of engaging communities of various scope and scale on how to best navigate PNT and imagine preferred pathways toward the future(s), Sardar and Sweeney published an article in the journal Futures outlining The Three Tomorrows method, which fills a gap in the field as "many methods of futures and foresight seldom incorporate pluralism and diversity intrinsically in their frameworks, and few, if any, emphasize the dynamic and merging nature of futures possibilities, or highlight the ignorance and uncertainties we constantly confront". == Theory and criticism == Rakesh Kapoor criticized PNT in 2011 as a Western concept that does not apply to India and other emerging markets. Sam Cole criticised the three Cs of PNT (chaos, complexity and contradictions) as "Alliterative Logic, theorizing through alliterative word-triads that is not based on empirical evidence". Jay Gary has suggested that PNT is embryonic, needs a more robust framework, and should be


