Bibliographic record
Abstract
Based on a systematic analysis of nearly 400 publications, this review article identifies four contrasting perspectives on evidence-based policy (EBP). One school of thought advocates reinforcing demands that governments pay more attention to research. A second perspective argues for the reform of the relationships between researchers and policy-makers. A third emphasises the need to reinvent formal procedures that govern the generation and use of evidence. The fourth rejects the possibility that research can simultaneously meet disciplinary standards and meaningfully address the needs of policy-makers. The paper concludes that to respond to the challenges facing EBP, researchers must develop a more realistic grasp of the task environment in which ministers and senior officials operate, reject naïve but prevalent assumptions about the level of analytical rationality in government, and recognise that direct and sustained engagement with policy-makers may not be compatible with career advancement in academia.
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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 | 0.121 | 0.322 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.005 | 0.030 |
| Scholarly communication | 0.019 | 0.056 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.025 | 0.034 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".