Why are people antiscience, and what can we do about it?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
From vaccination refusal to climate change denial, antiscience views are threatening humanity. When different individuals are provided with the same piece of scientific evidence, why do some accept whereas others dismiss it? Building on various emerging data and models that have explored the psychology of being antiscience, we specify four core bases of key principles driving antiscience attitudes. These principles are grounded in decades of research on attitudes, persuasion, social influence, social identity, and information processing. They apply across diverse domains of antiscience phenomena. Specifically, antiscience attitudes are more likely to emerge when a scientific message comes from sources perceived as lacking credibility; when the recipients embrace the social membership or identity of groups with antiscience attitudes; when the scientific message itself contradicts what recipients consider true, favorable, valuable, or moral; or when there is a mismatch between the delivery of the scientific message and the epistemic style of the recipient. Politics triggers or amplifies many principles across all four bases, making it a particularly potent force in antiscience attitudes. Guided by the key principles, we describe evidence-based counteractive strategies for increasing public acceptance of science.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it