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Sub-state nationalism and the welfare state: Québec and Canadian federalism

2006· article· en· W1915401523 on OpenAlexaffabout
Daniel Béland, André Lecours

Bibliographic record

VenueNations and Nationalism · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Systems and Governance
Canadian institutionsConcordia UniversityUniversity of Calgary
Fundersnot available
KeywordsNationalismSolidarityWelfare statePolitical economyPolitical sciencePoliticsState (computer science)FederalismNational identityDecentralizationSociologyEconomic systemPublic administrationEconomicsLaw

Abstract

fetched live from OpenAlex

ABSTRACT. This article examines the relationship between sub-state nationalism and the welfare state through the case of Québec in Canada. It argues that social policy presents mobilisation and identity-building potential for sub-state nationalism, and that nationalist movements affect the structure of welfare states. Nationalism and the welfare state revolve around the notion of solidarity. Because they often involve transfers of money between citizens, social programmes raise the issue of the specific community whose members should exhibit social and economic solidarity. From this perspective, nationalist movements are likely to seek the congruence between the ‘national community’ (as conceptualised by their leaders) and the ‘social community’ (the community where redistributive mechanisms should operate). Moreover, the political discourse of social policy lends itself well to national identity-building because it is typically underpinned by collective values and principles. Finally, pressures stemming from sub-state nationalism tend to reshape the policy agenda at both the state and the sub-state level while favouring the asymmetrical decentralisation of the welfare state.

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)0.002
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 score0.839
Threshold uncertainty score0.974

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0160.008
Scholarly communication0.0060.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.000

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 head0.007
GPT teacher head0.243
Teacher spread0.236 · 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

Citations70
Published2006
Admission routes2
Has abstractyes

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