MétaCan
Menu
Back to cohort
Record W2196047109 · doi:10.1111/gove.12179

How Solutions Chase Problems: Instrument Constituencies in the Policy Process

2015· article· en· W2196047109 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGovernance · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicPolicy Transfer and Learning
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsArticulation (sociology)Relevance (law)Process (computing)Context (archaeology)Promotion (chess)Evidence-based policyPolicy analysisPublic policyPolicy SciencesPolitical scienceManagement sciencePositive economicsEconomicsSociologyPublic administrationComputer sciencePoliticsLaw

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.868
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.102
GPT teacher head0.335
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it