What shapes the influence evidence has on policy? The role of politics in research utilisation
Bibliographic record
Abstract
What shapes the influence evidence has on policy? The key lesson that emerges from this paper is the primacy of politics in shaping how evidence is used. In order to influence the policy process, the research community must understand both the technocratic and the political aspects of policymaking, and how these shape the choices and incentives of policy elites. The paper proposes guidelines for integrating political economy analysis into different stages of the research and communication process. It addresses three main questions: \n\n \n\t What are the assumptions behind and problems with the concept of evidence-based policy and what can be learnt from this? \n\t What prevents the effective utilisation of research in policymaking? \n\t How can we put into practice what we know about the role of politics in shaping how evidence is used? \n \n\n The paper draws on some examples from Young Lives, a longitudinal study of childhood poverty in Ethiopia, India, Vietnam and Peru, and contains case studies of how researchers engaged with policymakers.
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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 | 0.215 | 0.364 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.009 | 0.070 |
| Scholarly communication | 0.045 | 0.028 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.014 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".