The Mobilization of Scientific Evidence by Public Policy Analysts
Bibliographic record
Abstract
Research on knowledge mobilization in policy making has been largely focused on identifying relevant factors having an effect on the uptake of evidence by actors and organizations. However, evidence on the magnitude of those effects remains limited and existing methods allowing for this have been scarcely used in this field. In this article, we first provide a rationale for greater investigation of substantive effect sizes, using methods such as mediation analysis and conditional probabilities. Using cross-sectional data from Québec (Canada) government policy analysts, we test an absorptive capacity model and describe direct, specific indirect, and total effects estimated from a path analysis. The results show that some factors have considerable effects, such as physical access and individual field of training, whereas some mediated relations are worth considering. Finally, we discuss some practical implications with regard to policy making and policy analysis but also the methodological standards of empirical research in this field.
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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 | 0.274 | 0.568 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.029 | 0.014 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".