---
title: "Public awareness of science"
chunk: 2/3
source: "https://en.wikipedia.org/wiki/Public_awareness_of_science"
category: "reference"
tags: "science, encyclopedia"
date_saved: "2026-05-05T03:25:34.929290+00:00"
instance: "kb-cron"
---

=== Civic science model ===
Some scholars have identified a new era of "post-normal science" (PNS) in which many scientific discoveries carry high stakes if risks are estimated incorrectly within a broader social context that has a high degree of uncertainty. This PNS era requires a new approach to public engagement efforts and requires a reevaluation of the underlying assumptions of "public engagement", especially with emerging science and technology issues, like CRISPR gene editing, that have the potential to become "wicked problems". These "wicked"  issues often require regulatory and policy decisions that have no single correct solution and often involve numerous interest groups – none of whom are clearly positioned to decide and resolve the problem. Policy and regulatory decisions around these scientific issues are inherently political and must balance trade-offs between the scientific research, perceptions of risk, societal needs, and ethical values. While scientists can provide factual answers to research questions and mathematical estimates of risk, many considerations surrounding these wicked science and technology issues have no factual answer. The unidirectional deficit model of simply educating the public on theses issues is insufficient to address these complex questions, and some scholars have proposed scientists adopt a culture of civic science: "broad public engagement with issues that arise at the many intersections between science and society." An emphasis is placed on developing an iterative engagement model that actively seeks to incorporate groups who stand to be adversely effected by a new technology and conducting this engagement away from universities so that it can be done on the public's terms with the public's terms. Other scholars have emphasized that this model of public engagement requires that the public be able to influence science, not merely be engaged by it, up to the point of being able to say "no" to research that does not align with the broader public's values. Under the civic science model, there are five key lessons for scientists committed to public engagement:

Establish why you want to engage with the public and clearly identify your goals.
Seek out and engage with a broad, diverse range of groups and perspectives and center engagement on listening to these groups.
Work cooperatively with groups to establish common definitions to avoid the perception that researchers are being disingenuous by relying on semantic differences between expert and lay interpretations of vocabulary to ensure the public "supports" their position.
Working to tilt public debates in favor of the priorities and values of researchers will not lead to consistent "best" decisions because wicked science and technology problems will have different considerations and perspectives depending on the application and cultural context.
Meaningfully engage as early as possible; engagement must begin early enough in the research process that the public's views can shape both the research and implementation of findings

== Public understanding of science ==

Social scientists use various metrics to measure public understanding of science, including:

=== Factual knowledge ===
The key assumptions is that the more individual pieces of information a person is able to retrieve, the more that person is considered to have learned.
Examples of measurement:

Recognition: Answering a specific question by selecting the correct answer out a list
Cued recall: Answering a specific question without a list of choices
Free recall: After exposure to information, the study participant produces a list of as much of the information as they can remember

=== Self-reported knowledge, perceived knowledge, or perceived familiarity ===
The key assumption is that emphasizes the value of knowledge of one's knowledge.
Examples of measurement:

Scaled survey responses to questions such as, "How well informed you would say you are about this topic?", this can be also used to assess perceived knowledge before and after events

=== Structural knowledge ===
The nature of connections among different pieces of information in memory.
The key assumption is that the use of elaboration increases the likelihood of remembering information.
Examples of measurement:

Asking study participants to assess relationships among concepts. For example, participants free recall concepts onto the first row and column of a matrix, then indicate whether the concepts are related to each other by placing an "X" in the cell if they are not. Participants then rank the remaining open cells by their relatedness from 1 (only very weakly) to 7 (very strongly related).
Study participants answer questions designed to measure elaboration involved in a task, such as, "I tried to relate the ideas I read about to my own past experiences."

=== Trust and credibility ===
People may trust science or scientists to different degrees, or may find specific scientists or specific research to be more or less credible. These factors can be related to how science can be used to advance knowledge, and may also be related to how science is communicated, with trust formation playing a central role.  
Examples of measurement:

The 21-item Trust in Science and Scientists Inventory, which measures agreement/disagreement with statements like, "We can trust scientists to share their discoveries even if we don't like their findings."
Scientist-specific measures of agreement, such as "I would trust scientific information if I knew it came from this author."

=== Mixed use of measures ===
While some studies purport that factual and perceived knowledge can be viewed as the same construct, a 2012 study investigating public knowledge of nanotechnology supports separating their use in communications research, as they "do not reflect the same underlying knowledge structures". Correlations between them were found to be low and they were not predicted by the same factors. For example different types of science media use, television versus online, predicted different constructs.
Factual knowledge has been shown to be empirically distinct from structural knowledge.