When do decision makers listen (less) to experts? The Swiss government's implementation of scientific advice during the <scp>COVID</scp>‐19 crisis
Bibliographic record
Abstract
Abstract Under which conditions do politicians listen to scientific experts in a crisis? This study addresses this question by assessing how the Swiss government implemented 186 policy recommendations formulated by the National COVID‐19 Science Task Force (STF) to combat the spread of the virus and alleviate its impact on the health system, society and economy during the first year of the pandemic. Results of multiple regression analyses show that the impact of problem pressure on the propensity of the government to implement experts' recommendations varies over time: it was considerably larger during spring 2020 than afterwards. We argue that this reflects a change in status of the STF during the second phase of the pandemic: it was distanced from the political‐strategic level of the crisis management organization and its epistemic authority was increasingly questioned by political parties and interest groups. Policy scholars should thus give more attention to how rapidly the government's propensity to rely on expert advice can change.
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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 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".