Why did the influence of experts erode during the COVID-19 pandemic?
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
In the face of protracted crises like climate change or pandemics, the influence of expert scientific projections on public policy is crucial yet evolves over time. This study offers an empirical demonstration of a previously fragmented theory: the diminishing influence of scientific projections on policy over time. Using a comprehensive mixed-method analysis, the article studies the relationship between expert projections, policy stringency and public support in Quebec during the COVID-19 pandemic. Scientific projections that put forward worst-case scenarios have a considerable impact on policies made in the early stages of a crisis. However, as these catastrophic projections instil a sense of fatalism as the crisis lasts, they inadvertently lead to diminished public support for both the policies and the scientific projections themselves. The implications of these findings for scientists and experts are discussed, highlighting the importance of adapting projections and knowledge communication strategies as the crisis unfolds.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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