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Record W2567515152 · doi:10.71781/19819

Principle and prejudice : attitudes toward ethnic minorities in Quebec

2015· dissertation· en· W2567515152 on OpenAlexaboutno aff
Anja Kilibarda

Bibliographic record

VenueOpen MIND · 2015
Typedissertation
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsPrejudice (legal term)Ethnic groupPolitical scienceSocial psychologyPsychologyGender studiesSociologyLaw

Abstract

fetched live from OpenAlex

L’intégration des nouveaux immigrants pose un défi, et ce, particulièrement dans les nations infra-étatiques. En effet, les citoyens vivant dans ces contextes ont davantage tendance à percevoir les immigrants comme de potentielles menaces politiques et culturelles. Cependant, les différents groupes ethniques et religieux minoritaires ne représentent pas tous le même degré de menace. Cette étude cherche à déterminer si les citoyens francophones québécois perçoivent différemment les différents groupes ethniques et religieux minoritaires, et s’ils entretiennent des attitudes plus négatives envers ces groupes, comparativement aux autres Canadiens. Dans la mesure où ces attitudes négatives existent, l’étude cherche à comprendre si ces dernières sont basées principalement sur des préjugés raciaux ou sur des inquiétudes culturelles. Se fondant sur des données nationales et provinciales, les résultats démontrent que les francophones Québécois sont plus négatifs envers les minorités religieuses que les autres canadiens mais pas envers les minorités raciales, et que ces attitudes négatives sont fondées principalement sur une inquiétude liée la laïcité et à la sécurité culturelle. L’antipathie envers certaines minorités observée au sein de la majorité francophone au Québec semble donc être dirigée envers des groupes spécifiques, et se fondent sur des principes de nature davantage culturelle que raciale.

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)0.002
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: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.002
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.079
GPT teacher head0.383
Teacher spread0.304 · 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 routes1
Has abstractyes

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