Reconciling Streams and Stages:Avoiding Mixed Metaphors in the Characterization of Policy Processes
Bibliographic record
Abstract
Metaphors have much analytical purchase in the policy sciences - none more so than that of the notions of policy stages and multiple streams. Yet there are difficulties, even allowing for necessary parsimony, in applying these individual metaphors to policy processes. This paper attempts to combine metaphors about policy cycles and policy streams in order to provide a more realistic synthesis that can capture diverse factors such as changing governance norms, detailed program interventions and the sheer complexity of policy drivers – ranging from coalition building to ‘garbage can’ solutions. In doing so it examines the difficulties of mixing these two metaphors and the special difficulty of ascertaining how many streams should be conceptualised. We argue that three-stream models such as Kingdon’s may be well suited to understanding one specific stage of policy-making but require augmentation in order to effectively interpret the full set of variables affecting processes and outcomes occurring through multiple stages of policy making. The paper proposes a five stream ‘confluence’ model which highlights the interactions between and among streams as a more effective metaphorical construction retaining the essence of the Kingdon ‘stream’ image while also incorporating elements of the ‘cycle’ or ‘stages’ one. The confluence model, it is argued, retains the basic thrust and vocabulary developed by Kingdon while offering a more comprehensive and relevant metaphor for capturing the dynamics of public policy-making.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.031 |
| Scholarly communication | 0.011 | 0.031 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".