Streams and stages: Reconciling Kingdon and policy process theory
Bibliographic record
Abstract
Abstract Use of metaphors is a staple feature of how we understand policy processes – none more so than the use of ‘policy stages’/'cycles’ and ‘multiple streams’. Yet even allowing for the necessary parsimony of metaphors, the former is often criticised for its lack of ‘real world’ engagement with agency, power, ideology, turbulence and complexity, while the latter focuses only on agenda‐setting but at times has been utilised, with limited results, to understand later stages of the policy process. This article seeks to explore and advance the opportunities for combining both and applying them to the policy‐formation and decision‐making stages of policy making. In doing so it examines possible three, four and five stream models. It argues that a five stream confluence model provides the highest analytical value because it retains the simplicity of metaphors (combining elements of two of the most prominent models in policy studies) while also helping capture some of the more complex and subtle aspects of policy processes, including policy styles and nested systems of governance.
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.
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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.011 | 0.023 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".