Closer than they look at first glance: A systematic review and a research agenda regarding measurement practices for policy learning
Bibliographic record
Abstract
Learning is a cognitive and social dynamic through which diverse types of actors involved in policy processes acquire, translate and disseminate new information and knowledge about public problems and solutions. In turn, they maintain, strengthen or revise their policy beliefs and preferences. Despite the conceptual and theoretical developments over the last years, concerns about the measurement of policy learning remain persistent. Based on the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) approach, this article reports the results of a systematic review of the existing practices for measuring policy learning in the public administration and policy research. In addition to operationalizations, data sources, methods of analysis and levels of analysis, we examine how the reviewed articles deal with the processual nature of policy learning. We show that the existing measurement practices transcend the research streams on policy learning for the most part, which extends the argument developed by Dunlop and Radaelli (2018) that policy learning is an analytical framework of the policy process. Based on these results, we argue for more transparent operationalizations, discuss the strengths and weaknesses of direct and indirect measurement approaches, and call for more creativity in designing measurement methods that recognize the multilevel nature of policy learning.
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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 | 0.149 | 0.384 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.017 | 0.027 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.019 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".