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Record W3038250080 · doi:10.1111/nana.12641

Boundaries of the nation(s) in a multinational state: Comparing Quebecers and other Canadians' perspectives on national identity

2020· article· en· W3038250080 on OpenAlexafffundabout
Antoine Bilodeau, Luc Turgeon

Bibliographic record

VenueNations and Nationalism · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsUniversity of OttawaConcordia University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsMultinational corporationRest (music)ImmigrationNational identityState (computer science)SociologyIdentity (music)Gender studiesEthnic groupPolitical sciencePolitical economyLaw

Abstract

fetched live from OpenAlex

Abstract Drawing on the Canadian case, this study examines whether, in a multinational state, majority and minority nations emphasize different criteria when tracing the borders of their respective national community. It does so by comparing native‐born French speakers in Quebec and native‐born English speakers in the rest of Canada from three different perspectives. We examine (a) the way ascriptive and attainable groupings of characteristics are constructed in Quebec and in the rest of Canada, (b) the importance given to attainable and ascriptive characteristics, and (c) the implications of ascriptive and attainable characteristics for attitudes toward immigration and generalized trust. The findings suggest that majority‐group members in Quebec and in the rest of Canada broadly draw the boundaries of their nation in similar ways and with similar implications.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.272

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0150.007
Scholarly communication0.0050.001
Open science0.0010.003
Research integrity0.0010.001
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 head0.031
GPT teacher head0.315
Teacher spread0.284 · 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 designObservational
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

Citations14
Published2020
Admission routes3
Has abstractyes

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