Do Citizens Even Want to Hear the Truth? Public Attitudes Towards Evidence- Informed Policy Making
Bibliographic record
Abstract
Evaluations-informed policy making is often glorified to be the true approach to introduce and evaluate public policies.Yet is this view also shared by the public?In this chapter, we consider the question which attitudes citizens have toward scientific evidence and how these differ across political systems and individual characteristics.We present the results of a cross-sectional survey among some 9000 citizens in six countries (Australia, Belgium, Canada, France, Switzerland and the United States) that has been conducted in the middle of the COVID-19 pandemic (2020/2021).The survey shows that public support for evidence substantially varies across countries and individuals.Post-truth countries show strong political polarization regarding the attitudes towards evidence-informed policy making.As we discuss, these findings might have import implications for evaluators and future research on evaluation, as they prompt the need for a paradigm shift toward more public participation in evaluations.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".