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Record W3101524718 · doi:10.1080/21622671.2020.1837225

Ideas, federalism and policy feedback: an institutionalist approach

2020· article· en· W3101524718 on OpenAlexaffabout
Daniel Béland, André Lecours

Bibliographic record

VenueTerritory Politics Governance · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Policy and Reform Studies
Canadian institutionsUniversity of OttawaMcGill University
Fundersnot available
KeywordsFederalismPoliticsDimension (graph theory)Political sciencePublic administrationCooperative federalismWork (physics)Public policyIdentity (music)Historical institutionalismSociologyFocus (optics)Positive economicsEconomicsLaw

Abstract

fetched live from OpenAlex

Federalism and policy feedback are central issues within the institutionalist tradition, but scholars who study the role of ideas within that tradition have paid surprisingly limited attention to these two issues. More specifically, within the public policy literature, little has been published about the relationship between federalism and the ideational dimension of policy feedback. The objective of this article is to explore this relationship at the theoretical level before illustrating basic analytical claims about it through empirical work. The empirical sections draw on two case studies from Canada to illustrate the interaction between federalism and ideational policy feedback over time: provincial social policy, with a focus on Quebec, and the federal National Energy Program, with a focus on its impact on Alberta. The analysis stresses the role of identity formation in mediating the connection between ideational and institutional processes in federal political systems.

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 imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.027
Threshold uncertainty score0.193

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.004
Science and technology studies0.0060.059
Scholarly communication0.0140.011
Open science0.0020.006
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.044
GPT teacher head0.322
Teacher spread0.278 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2020
Admission routes2
Has abstractyes

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