Moving policy implementation theory forward: A multiple streams/critical juncture approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it