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Record W2229611050 · doi:10.1111/ropr.12156

Institutional Change Through Policy Learning: The Case of the European Commission and Research Policy

2016· article· en· W2229611050 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueReview of Policy Research · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicPolicy Transfer and Learning
Canadian institutionsYork University
FundersSocial Sciences and Humanities Research Council of CanadaEuropean CommissionYork University
KeywordsFraming (construction)Policy learningEuropean commissionCorporate governanceFoundation (evidence)CommissionPolitical scienceInstitutional changePublic administrationResearch policyEmpirical researchPublic relationsBusinessEconomicsManagementEuropean unionEpistemology

Abstract

fetched live from OpenAlex

Abstract Research initiatives to enhance knowledge‐based societies demand regionally coordinated policy approaches. By analyzing the case of the European Commission, Directorate‐General Research and Innovation, this study focuses on examining the cognitive mechanisms that form the foundation for institutional transformations and result in leadership positions in regional governance. Drawing on policy learning theories, the study emphasizes specific mechanisms of institutional change that are often less noticeable but can gradually lead to mobilizing diverse groups of stakeholders. Through historical and empirical data, this study shows the importance of policy learning through communication processes, Open Method of Coordination initiatives, and issue framing in creating a stronger foundation for policy coordination in European research policy since the 2000s.

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)0.022
metaresearch head score (Gemma)0.022
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.973
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0220.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.004
Science and technology studies0.0030.005
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.400
GPT teacher head0.572
Teacher spread0.172 · 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