Instrument constituencies and public policy-making: an introduction
Bibliographic record
Abstract
Abstract For many years, policy-making has been envisioned as a process in which subsets of policy actors engage in specific types of interactions involved in the definition of policy problems, the articulation of solutions and their matching or enactment. This activity involves the definition of policy goals (both broad and specific), the creation or identification of the means and mechanisms that need to be implemented to realize these goals, and the set of bureaucratic, partisan, electoral and other political struggles involved in their acceptance and transformation into action. While past research on policy subsystems has often assumed or implied that these tasks could be undertaken by any actor, more recent research argues that distinct sets of actors are involved in these three tasks: epistemic communities that are engaged in discussions about policy dilemmas and problems; instrument constituencies that define and promote policy instruments and alternatives; and advocacy coalitions which compete to have their choice of policy alternative and problem frames adopted. Two of these three sets of actors are quite well known and, indeed, have their own literature about what it takes to be a member of an epistemic community or advocacy coalition, although interactions between the two are rarely discussed. The third subset, the instrument constituency, is much less known but has from the outset been considered in relation to these other policy actors. The articles in this special issue focus on better understanding the nature of actor interactions undertaken by instrument constituencies and how these relate to the other kinds of actors involved in 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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".