Who Is a Stream? Epistemic Communities, Instrument Constituencies and Advocacy Coalitions in Public Policy-Making
Bibliographic record
Abstract
John Kingdon’s Multiple Streams Framework (MSF) was articulated in order to better understand how issues entered into policy agendas, using the concept of a policy actors interacting in course of sequences of events occurring in what he referred to as the "problem", "policy" and "politics" "streams". In this study Kingdon used an undifferentiated concept of a ‘policy subsystem’ to organize the activities of various policy actors involved in this process. However, it is not a priori certain who the agents are in this process and how they interact. This paper argues the policy world can also be visualized as being composed of different distinct subsets of subsystem actors who engage over specific sets of interactions over the definition of policy problems, the articulation of solutions and their matching or enactment. Using this lens, this article focuses on actor interactions involved in policy formulation activities occurring immediately following the agenda setting stage upon which Kingdon originally worked. This activity involves the definition of policy goals (both broad and specific) and the creation of the means and mechanisms to realise these goals. The article argues this stage is best analyzed form the perspective of three separate sets of actors involved in these tasks: the epistemic community which finds itself engaged in discourses about policy problems; the activities of instrument constituencies which define the policy stream in which policy alternatives and instruments are formulated; and that of advocacy coalitions which make up the politics stream as they compete to have their choice of policy alternatives selected by decision makers. The article argues these different sets of policy actors personify each of Kingdon’s three different streams of policy, problem and politics and that extending Kingdon’s work to the examination of policy formulation using this basic vocabulary yields superior insights into policy formulation than other extant models.
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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 | 0.026 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.016 | 0.047 |
| Scholarly communication | 0.024 | 0.044 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".