Moving policy implementation theory forward: A multiple streams/critical juncture approach
Bibliographic record
Abstract
Meta-reviews of the implementation literature have constantly bemoaned a lack of theory in this area. This is partially a function of the policy sciences having inherited a tradition of descriptive work in public administration, a historical phenomenon exacerbated by the more recent addition to this corpus of an equally atheoretical set of works in public management. As a result, the study of policy implementation within the policy sciences remains fractured and largely anecdotal, with a set of proto-theories competing for attention – from network management to principal–agent theory, game theory and others – while very loose frameworks like the ‘bottom-up vs. top-down’ debate continue to attract attention, but with little progress to show for more than 30 years of work on this subject. This article argues the way out of this conundrum is to revisit the subject and object of policy implementation through the lens of policy process theory, rather than appropriating somewhat ill-fitting concepts from other disciplines to this area of fields of study. In particular, it looks at the recent synthesis of several competing frameworks in the policy sciences – advocacy coalition, multiple streams and policy cycle models – developed by Howlett, McConnell and Perl and argues this approach, hitherto applied only to the ‘front end’ activities of agenda setting and policy formulation, helps better situate implementation activities in public policy studies, drawing attention to the different streams of actors and events active at this phase of public policy-making and helping to pull implementation studies back into the policy science mainstream.
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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 | 0.122 | 0.118 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.028 | 0.015 |
| Science and technology studies | 0.011 | 0.066 |
| Scholarly communication | 0.036 | 0.056 |
| Open science | 0.010 | 0.017 |
| Research integrity | 0.014 | 0.025 |
| Insufficient payload (model declined to judge) | 0.013 | 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".