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 onto policy agendas, using the concept of policy actors interacting over the course of sequences of events in what he referred to as the “problem”, “policy” and “politics” “streams”. However, it is not a priori certain who the agents are in this process and how they interact with each other. As was common at the time, in his study Kingdon used an undifferentiated concept of a “policy subsystem” to group together and capture the activities of various policy actors involved in this process. However, this article argues that the policy world Kingdon envisioned can be better visualized as one composed of distinct subsets of actors who engage in one specific type of interaction involved in the definition of policy problems: either the articulation of problems, the development of solutions, or their enactment. Rather than involve all subsystem actors, this article argues that three separate sets of actors are involved in these tasks: epistemic communities are engaged in discourses about policy problems; instrument constituencies define policy alternatives and instruments; and advocacy coalitions compete to have their choice of policy alternatives adopted. Using this lens, the article focuses on actor interactions involved both in the agenda-setting activities Kingdon examined as well as in the policy formulation activities following the agenda setting stage upon which Kingdon originally worked. This activity involves the definition of policy goals (both broad and specific), the creation of the means and mechanisms to realize these goals, and the set of bureaucratic, partisan, electoral and other political struggles involved in their acceptance and transformation into action. Like agenda-setting, these activities can best be modeled using a differentiated subsystem approach.
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 | 0.026 | 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".