How Solutions Chase Problems: Instrument Constituencies in the Policy Process
Bibliographic record
Abstract
Public policies are composed of complex arrangements of policy goals and policy means matched through some decision‐making process. Exactly how this process works and which comes first—problem or solution—is an outstanding research question in the policy sciences. This article argues the emerging concept of an “instrument constituency”—a subsystem component dedicated to the articulation and promotion of particular kinds of solutions regardless of problem context—can help policy scholars answer this critical question and better understand policymaking. At present, however, there is only limited empirical evidence of the existence, accuracy, and relevance of the instrument constituency concept. This article clarifies and refines the concept through cross‐sectoral and cross‐national case studies, demonstrating its utility in aiding our understanding of policy processes and their dynamics, including the issue of how problems and solutions are proposed and matched in the course of policy adoption.
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 | 0.033 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.014 | 0.040 |
| Scholarly communication | 0.021 | 0.031 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 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".