Sciences perceived as precise and consensual are more trusted

Why we don’t really know what the public thinks about science

Measuring trust isn’t enough. Furthering knowledge about the institutions and norms of science is the best way to build credibility.

Have people stopped trusting science? The data tell a surprising story

Some say there’s a global crisis of trust — but research reveals where the real problems lie.

Can we measure trust in scientific publications? - LSE Impact

Jonathon Alexis Coates outlines how a constellation of static and dynamic indicators could provide a means for assessing the trustworthiness of published research

How can citizen science reduce psychological distance to science? Insights from three projects in contested environmental contexts

Citizen science (CS) is a participatory mode of knowledge production, enabling non-scientific actors to contribute to and sometimes contest scientific agendas and interpretations, making it a way to bridge science and society. This paper examines how that potential unfolds by analysing the individual perspectives of citizen scientists through the lens of Psychological Distance to Science (PSYDISC). Drawing on three case studies of contested environmental CS, we identify which contextual aspects of CS shape citizen scientists' experiences of social, spatial, temporal, or hypothetical distance to relevant science, and how these experiences may relate to trust. Our findings underscore the role of science communication as both a channel for dissemination, and as a constitutive element of participatory research; crucial for reducing psychological distance and enabling socially robust knowledge production, especially in contested, policy-relevant science settings.

Illusions of Understanding in the Sciences

Scientists seek to understand the causes of observed phenomena. Beliefs that they have succeeded are based on understanding that is rarely or possibly never complete, and varies in depth and quality. Most often scientists believe they understand more than they do, making their belief an illusion. This illusion then persists in explanations scientists provide in print, in talks, or in discussions. The illusion that a scientist has a valid and complete explanation tends to be magnified when the data are well described by mathematical and computer simulation models due to the precision of such models and their ability to predict well; prediction does not imply causality, but gives the illusion that it does. The first part of this essay supports the case for the universality of partial and incomplete levels of understanding by showing the difficulty of reaching a deep level of understanding for even a simple analysis and model that most scientists use and believe they understand: linear regression. The second part highlights some implications of the existence of many levels of understanding and explanation, and their use by scientists for design, testing, analysis, and theory development. It discusses the way that deduction and induction depend on the levels of understanding and the implications of the illusion that a scientist’s understanding is deep. It makes a case that the many incomplete levels of understanding affect, often unwittingly, the ways scientists design experiments, test theories, comprehend, communicate, and teach.

How Field Experiments in Economics Can Complement Psychological Research on Judgment Biases

This review summarizes results of field experiments examining individual behaviors across several market settings—from open-air markets to rideshare markets to tax-compliance markets—where people sort themselves into market roles wherein they make consequential decisions. Using three distinct examples from my own research on the endowment effect, left-digit bias, and omission bias, I showcase how field experiments can help researchers understand mediators, heterogeneity, and causal moderation involved in judgment biases in the field. In this manner, the review highlights that economic field experiments can serve an invaluable intellectual role alongside traditional laboratory research.

Predictive processing frameworks for perception can explain recent drone sightings in the United States

We draw on the predictive processing theory of perception to explain why healthy, intelligent, honest, and psychologically normal people might easily misperceive lights in the sky as threatening or extraordinary objects, especially in the context of WEIRD (western, educated, industrial, rich, and democratic) societies. We argue that the uniquely sparse properties of skyborne and celestial stimuli make it difficult for an observer to update prior beliefs, which can be easily fit to observed lights. Moreover, we hypothesize that humans have likely evolved to perceive the sky and its perceived contents as deeply meaningful. Finally, we briefly discuss the possible role of generalized distrust in scientific institutions and ultimately argue for the importance of astronomy education for producing a society with prior beliefs that support veridical perception.

Faster science, penalties in evaluation, and concerns on quality and impact: Researchers’ use and perceptions of preprints

The preprint ecosystem has expanded rapidly over the past decade, fundamentally altering science communication. Yet, the scholarly community’s attitudes toward this shift remain underexplored. Through a large-scale survey of US and Canadian biomedical scholars, we provide a comprehensive analysis of preprint utilization, perceived impact, and integration into academic credit systems. We find robust engagement across reading, citing, and submitting preprints; however, this activity is driven primarily by a desire for rapid dissemination rather than a foundational commitment to open science. Furthermore, while preprints are valued as networking assets, perceived career penalties during formal academic evaluations stifle broader cultural adoption. Crucially, to navigate the absence of formal peer review, scholars report a heavy reliance on author reputation as a primary heuristic to evaluate a preprint’s credibility and guide their reading and citation decisions. Notably, despite acknowledging preprints’ role in accelerating knowledge sharing, scholars express significant concerns regarding fraud and misinformation, particularly amid declining public trust in science and emerging threats to scientific integrity from artificial intelligence. To resolve these tensions, the preprint ecosystem must evolve beyond prioritizing speed to foster genuine academic dialogue. Simultaneously, evaluation frameworks must adapt to the realities of preprinting, and innovative quality-control mechanisms are urgently needed to balance rapid dissemination with rigorous scientific integrity.

Unequal Scientific Recognition in the Age of LLMs

Large language models (LLMs) are reshaping how scientific knowledge is accessed and represented. This study evaluates the extent to which popular and frontier LLMs including GPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro recognize scientists, benchmarking their outputs against OpenAlex and Wikipedia. Using a dataset focusing on 100,000 physicists from OpenAlex to evaluate LLM recognition, we uncover substantial disparities: LLMs exhibit selective and inconsistent recognition patterns. Recognition correlates strongly with scholarly impact such as citations, and remains uneven across gender and geography. Women researchers, and researchers from Africa, Asia, and Latin America are significantly underrecognized. We further examine the role of training data provenance, identifying Wikipedia as a potential sources that contributes to recognition gaps. Our findings highlight how LLMs can reflect, and potentially amplify existing disparities in science, underscoring the need for more transparent and inclusive knowledge systems.

Why Most Published Research Findings Are False

Summary There is increasing concern that most current published research findings are false. The probability that a research claim is true may depend on study power and bias, the number of other studies on the same question, and, importantly, the ratio of true to no relationships among the relationships probed in each scientific field. In this framework, a research finding is less likely to be true when the studies conducted in a field are smaller; when effect sizes are smaller; when there is a greater number and lesser preselection of tested relationships; where there is greater flexibility in designs, definitions, outcomes, and analytical modes; when there is greater financial and other interest and prejudice; and when more teams are involved in a scientific field in chase of statistical significance. Simulations show that for most study designs and settings, it is more likely for a research claim to be false than true. Moreover, for many current scientific fields, claimed research findings may often be simply accurate measures of the prevailing bias. In this essay, I discuss the implications of these problems for the conduct and interpretation of research.

Against theory-motivated experimentation: Can random experimental choice lead to better theories?

Scientists must choose which among many experiments to perform. We study the epistemic success of experimental choice strategies proposed by philosophers of science or executed by scientists themselves. We develop a multi-agent model of the scientific process that jointly formalizes its core aspects: active experimentation, theorizing, and social learning. We find that agents who choose new experiments at random develop the most informative and predictive theories of the world. The agents aiming to confirm, falsify theories, or resolve theoretical disagreements end up with an illusion of epistemic success: they develop promising accounts for the data they collected, while misrepresenting the ground truth that they intended to learn about. Agents experimenting in these theory-motivated ways acquire less diverse or less representative samples from the ground truth that also turn out to be easier to account for. Random data collection, on the other hand, combines virtues of diverse and representative sampling from a target scientific domain which enables cumulative development of the successful theoretical accounts of it. We suggest that randomization, already a gold standard within experiments, is also beneficial at the level of experiments themselves.

Science as Collective Sensemaking

What is Science?