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Record W3157557180 · doi:10.1111/cars.12339

Perceived racial and cultural discrimination and sense of belonging in Canadian society

2021· article· en· W3157557180 on OpenAlexaffabout
Zheng Wu, Maria Sigridur Finnsdottir

Bibliographic record

VenueCanadian Review of Sociology/Revue canadienne de sociologie · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicRacial and Ethnic Identity Research
Canadian institutionsUniversity of TorontoSimon Fraser University
Fundersnot available
KeywordsMulticulturalismWhite (mutation)RacismRace (biology)Social psychologyEthnic groupSociologySense of placeSense of communityIdentification (biology)Gender studiesPsychologySocial scienceAnthropology

Abstract

fetched live from OpenAlex

Multiculturalism promises equality and tolerance, yet racialized minorities in Canada continue to report experiences of discrimination. As Canada becomes increasingly culturally and racially diverse, it is important to understand what this discrimination means for sense of belonging in Canada. Using ordinary logistic regressions, we examine the effects of ethnocultural and racial discrimination on sense of belonging. Relying on a theoretical framework of the Rejection/Identification and Rejection/Disidentification models, we test the impacts of discrimination on national sense of belonging and on in-group sense of belonging. We further examine the differential effects of discrimination on sense of belonging for white and non-white Canadians. We find that discrimination negatively impacts both national and in-group sense of belonging among both non-white and white Canadians, although the impact is stronger among racialized minorities. Thus, we argue that discrimination reduces sense of belonging in Canada generally, but is more damaging to those who already occupy a marginalized social position. These findings have implications for our understanding of multiculturalism in Canada.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.332
Threshold uncertainty score0.964

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.058
GPT teacher head0.348
Teacher spread0.289 · 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 teacher head, 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

Citations18
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Review of Sociology/Revue canadienne de sociologieSame topicRacial and Ethnic Identity ResearchFrench-language works237,207