Trust, but verify? Understanding citizen attitudes toward evidence‐informed policy making
Bibliographic record
Abstract
Abstract In this article, we inquire to what extent different manifestations of trust are associated with public support for evidence informed policy making (EIPM). We present the results of a cross‐sectional survey conducted in the peak of the second COVID‐19 wave in six Western democracies: Australia, Belgium, Canada, France, Switzerland, and the United States (N = 8749). Our findings show that public trust in scientific experts is generally related to positive attitudes toward evidence‐informed policy making, while the opposite is the case for trust in governments and fellow citizens. Interestingly, citizens' assessment of government responses to COVID‐19 moderates the relationship between trust and attitudes toward EIPM. Respondents who do rather not trust their governments or their fellow citizens are more in favor of EIPM if they evaluate government responses negatively. These findings suggest that attitudes toward EIPM are not only related to trust, but also strongly depend on perceived government performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".