Instrument constituencies and public policy-making: an introduction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it