---
title: "Metascience"
chunk: 2/7
source: "https://en.wikipedia.org/wiki/Metascience"
category: "reference"
tags: "science, encyclopedia"
date_saved: "2026-05-05T03:44:21.146005+00:00"
instance: "kb-cron"
---

=== 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 fields.
Moreover, replication of research (or failure to replicate) is considered less influential than original research, and is less likely to be published in many fields. This discourages the reporting of, and even attempts to replicate, studies.

=== Evaluation and incentives ===

Metascience seeks to create a scientific foundation for peer review. Meta-research evaluates peer review systems including pre-publication peer review, post-publication peer review, and open peer review. It also seeks to develop better research funding criteria.
Metascience seeks to promote better research through better incentive systems. This includes studying the accuracy, effectiveness, costs, and benefits of different approaches to ranking and evaluating research and those who perform it. Critics argue that perverse incentives have created a publish-or-perish environment in academia which promotes the production of junk science, low quality research, and false positives. According to Brian Nosek, "The problem that we face is that the incentive system is focused almost entirely on getting research published, rather than on getting research right." Proponents of reform seek to structure the incentive system to favor higher-quality results. For example, by quality being judged on the basis of narrative expert evaluations ("rather than [only or mainly] indices"), institutional evaluation criteria, guaranteeing of transparency, and professional standards.

==== Contributorship ====
Studies proposed machine-readable standards and (a taxonomy of) badges for science publication management systems that hones in on contributorship – who has contributed what and how much of the research labor – rather than using the traditional concept of plain authorship – who was involved in any way in the creation of a publication. A study pointed out one of the problems associated with the ongoing neglect of contribution nuanciation – it found that "the number of publications has ceased to be a good metric as a result of longer author lists, shorter papers, and surging publication numbers".

==== Assessment factors ====
Factors other than a submission's merits can substantially influence peer reviewers' evaluations. Such factors may however also be important such as the use of track-records about the veracity of a researchers' prior publications and its alignment with public interests. Nevertheless, evaluation systems – include those of peer-review – may substantially lack mechanisms and criteria that are oriented or well-performingly oriented towards merit, real-world positive impact, progress and public usefulness rather than analytical indicators such as number of citations or altmetrics even when such can be used as partial indicators of such ends. Rethinking of the academic reward structure "to offer more formal recognition for intermediate products, such as data" could have positive impacts and reduce data withholding.

==== Recognition of training ====
A commentary noted that academic rankings don't consider where (country and institute) the respective researchers were trained.

==== Scientometrics ====

Scientometrics concerns itself with measuring bibliographic data in scientific publications. Major research issues include the measurement of the impact of research papers and academic journals, the understanding of scientific citations, and the use of such measurements in policy and management contexts. Studies suggest that "metrics used to measure academic success, such as the number of publications, citation number, and impact factor, have not changed for decades" and have to some degrees "ceased" to be good measures, leading to issues such as "overproduction, unnecessary fragmentations, overselling, predatory journals (pay and publish), clever plagiarism, and deliberate obfuscation of scientific results so as to sell and oversell".
Novel tools in this area include systems to quantify how much the cited-node informs the citing-node. This can be used to convert unweighted citation networks to a weighted one and  then for importance assessment, deriving "impact metrics for the various entities involved, like the publications, authors etc" as well as, among other tools, for search engine- and recommendation systems.

==== Science governance ====

Science funding and science governance can also be explored and informed by metascience.

===== Incentives =====
Various interventions such as prioritization can be important. For instance, the concept of differential technological development refers to deliberately developing technologies – e.g. control-, safety- and policy-technologies versus risky biotechnologies – at different precautionary paces to decrease risks, mainly global catastrophic risk, by influencing the sequence in which technologies are developed. Conventional legislative and incentive structures may be insufficient to ensure proper scientific governance because they often respond too slowly or inadequately to emerging challenges.
Other incentives to govern science and related processes, including via metascience-based reforms, may include ensuring accountability to the public (in terms of e.g. accessibility of, especially publicly-funded, research or of it addressing various research topics of public interest in serious manners), increasing the qualified productive scientific workforce, improving the efficiency of science to improve problem-solving in general, and facilitating that unambiguous societal needs based on solid scientific evidence – such as about human physiology – are adequately prioritized and addressed. Such interventions, incentives and intervention-designs can be subjects of metascience.