Persistent misunderstandings about evidence-based (sorry: informed!) policy-making
Bibliographic record
Abstract
BACKGROUND: The field of research on knowledge mobilization and evidence-informed policy-making has seen enduring debates related to various fundamental assumptions such as the definition of 'evidence', the relative validity of various research methods, the actual role of evidence to inform policy-making, etc. In many cases, these discussions serve a useful purpose, but they also stem from serious disagreement on methodological and epistemological issues. DISCUSSION: This essay reviews the rationale for evidence-informed policy-making by examining some of the common claims made about the aims and practices of this perspective on public policy. Supplementing the existing justifications for evidence-based policy making, we argue in favor of a greater inclusion of research evidence in the policy process but in a structured fashion, based on methodological considerations. In this respect, we present an overview of the intricate relation between policy questions and appropriate research designs. SUMMARY: By closely examining the relation between research questions and research designs, we claim that the usual points of disagreement are mitigated. For instance, when focusing on the variety of research designs that can answer a range of policy questions, the common critical claim about 'RCT-based policy-making' seems to lose some, if not all of its grip.
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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 | 0.413 | 0.507 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.008 | 0.136 |
| Scholarly communication | 0.044 | 0.044 |
| Open science | 0.011 | 0.017 |
| Research integrity | 0.030 | 0.061 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".