Have policy process scholars embraced causal mechanisms? A review of five popular frameworks
Bibliographic record
Abstract
Over 30 years, several key frameworks and theories of the policy process have emerged which have guided a burgeoning empirical literature. A more recent development has been a growing interest in the application of a ‘causal mechanism’ perspective to policy studies. This article reviews selected theories of the policy process (Multiple Streams Approach, Advocacy Coalition Framework, Punctuated Equilibrium Theory, Narrative Framework Theory, and Institutional Analysis and Development Framework) and reports on an exploratory meta-analysis and synthesis to gauge the take-up of causal-mechanistic approaches. The findings suggest that there has been limited application of causal mechanisms and calls for more theoretical and empirical work on that aspect. Given the overlapping frameworks exploring different aspects of the policy process, further research informed by causal-mechanism approaches points to a new generation of inquiry across these and other policy process theoretical frameworks.
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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 | 0.050 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.022 | 0.033 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.011 | 0.020 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.007 | 0.009 |
| 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".