MétaCan
Menu
Back to cohort

How Big Is a Policy Network? An Assessment Utilizing Data From Canadian Royal Commissions 1970–2000

2006· article· en· W2004107264 on OpenAlex
Michael Howlett, Anthony Maragna

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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueReview of Policy Research · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Policy and Reform Studies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsOperationalizationConsistency (knowledge bases)MetaphorWork (physics)Replication (statistics)EstimationComputer scienceBig dataManagement scienceSociologyOperations researchPolitical scienceData scienceEpistemologyEconomicsManagementData miningEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract The subsystem approach to policy studies is now well established in theory. Despite many applications to empirical cases, however, many elements of the operationalization of this approach have remained problematic, prompting some critics to reject it as “unscientific.” Although the approach has been defended as “more than a metaphor,” it is certainly apparent that additional work is required to address fundamental aspects of the model and ensure that its application to specific cases is done in such a way as to meet basic methodological prerequisites of consistency and replication. This article builds on earlier work by one of the authors attempting to address some of these concerns. Specifically, it addresses issues surrounding the methods through which subsystem membership can be identified and attempts some preliminary conclusions with respect to the estimation of average subsystem size in contemporary advanced liberal democracies.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.651
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.001
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 head0.343
GPT teacher head0.568
Teacher spread0.225 · 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