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Record W2572838411 · doi:10.1017/s0008423916001098

Ideas, Institutions and the Politics of Federalism and Territorial Redistribution

2016· article· en· W2572838411 on OpenAlexaffabout
Daniel Béland, André Lecours

Bibliographic record

VenueCanadian Journal of Political Science · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Policy and Reform Studies
Canadian institutionsUniversity of OttawaUniversity of Saskatchewan
Fundersnot available
KeywordsFederalismNationalismRedistribution (election)ResentmentPoliticsPolitical scienceDiscretionPolitical economyFlemishPublic administrationWork (physics)SociologyLawGeography

Abstract

fetched live from OpenAlex

Abstract Drawing on the work of Richard Simeon and using the cases of equalization in Canada and Belgium's social security system, this article shows how nationalist ideas combined with institutional management structures, government formation rules and the configuration of party systems to condition the territorial dynamics around these two programmes. In Canada, resentment against equalization in many provinces, often because it is perceived as accommodating Québécois nationalism, has translated only into moderate pressures on the programme because federal parties have largely stayed away from this divisive issue and federal executive discretion over the programme has meant that provinces cannot force change. In Belgium, pressures on social security have been more intense because the absence of pan-Belgian parties has given greater resonance to Flemish nationalist ideas within the political system.

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)0.004
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.972
Threshold uncertainty score0.281

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0080.050
Scholarly communication0.0100.003
Open science0.0010.003
Research integrity0.0020.002
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 head0.029
GPT teacher head0.330
Teacher spread0.301 · 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 designNot applicable
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

Citations20
Published2016
Admission routes2
Has abstractyes

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