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Record W2361960295

Le déplacement des frontières citoyennes québécoises : des récits identitaires en tension

2015· article· fr· W2361960295 on OpenAlexfundaboutno aff
Claudie Thibaudeau

Bibliographic record

VenueArchipelago (Université du Québec à Montréal) · 2015
Typearticle
Languagefr
FieldSocial Sciences
TopicSocial Sciences and Governance
Canadian institutionsnot available
FundersMcGill University Health CentreMcGill University
KeywordsHumanitiesPolitical scienceSociologyPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

Depuis plus d'une trentaine d'années, le Québec aurait entrepris un projet « interculturel ». Le dépôt du projet de loi 60, soit la Charte affirmant les valeurs de laïcité et de neutralité religieuse de l'État ainsi que d'égalité entre les femmes et les hommes et encadrant les demandes d'accommodement, par le gouvernement québécois nous rappelle pourtant que l'interculturalisme est loin de faire l'unanimité parmi les intellectuels-les, les acteur-trices politiques et les acteurs-es sociaux-ales au Québec. Dans le cadre de cette recherche, nous analysons les différents récits sur citoyenneté et la diversité, aux sensibilités pluralistes variables, qui se font concurrence à la lumière de ce débat. Par une analyse du discours public, nous souhaitons révéler le caractère hétérogène et discontinu des récits sur l'identité québécoise dans un contexte de diversité. Adoptant une perspective pluraliste et agonistique, nous soutenons que les luttes au sujet de la reconnaissance constituent des espaces politiques de redéfinition continue des frontières de la citoyenneté. \n______________________________________________________________________________ \nMOTS-CLÉS DE L’AUTEUR : citoyenneté, diversité, récits identitaires, interculturalisme, pluralisme agonistique

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)0.001
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.099
Threshold uncertainty score0.268

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0280.014
Scholarly communication0.0090.003
Open science0.0010.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0140.001

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 head0.017
GPT teacher head0.215
Teacher spread0.198 · 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

Citations0
Published2015
Admission routes2
Has abstractyes

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