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Record W4401461203 · doi:10.1353/ces.2024.a934424

Everyday Experiences of Racial Discrimination Among Chinese Immigrants in Canada

2024· article· en· W4401461203 on OpenAlexvenueaboutno aff
Zhang Weiguo, Weijia Tan, Jinhua Chen, Zhuo Jun Zhong, Kunping Wang, Kedi Zhao

Bibliographic record

VenueCanadian ethnic studies · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationRacismChinese americansEthnologySociologyGender studiesHistoryArchaeology

Abstract

fetched live from OpenAlex

Abstract: This study examined the everyday experiences of racism among Chinese immigrants in Canada. Data came from nine virtual focus groups comprising 48 participants of diverse genders and ages taking place in December 2021 and January 2022. We conducted our qualitative analysis by drawing on Essed's conceptualization of everyday racism and Crenshaw's conceptualization of intersectionality. Thematic and content analysis showed that racism against Chinese immigrants is more pervasive than previously recognized. Three-quarters of the participants disclosed instances of racism across various settings, including workplaces, schools, shops, airports, banks, hotels, restaurants, streets, and online. Some gender and age groups reported more incidents than others. Young men, on average, encountered the greatest number, followed by young and middle-aged women, while older men reported the fewest. Types of racist incidents also varied by age and gender. Young and middle-aged women encountered unsolicited intimate remarks and racism related to gender division of labour, while older adults, both men and women, faced racism associated with service provision. Younger men reported online racism. Some older participants remained unaware of the racist nature of their encounters, and middle-aged and older men tended to deny having experienced racist encounters. Given these findings, we suggest the need to raise awareness, establish empowerment initiatives, and adopt intersectionality approaches to address and combat racism against Chinese immigrants in Canada. Résumé: Cette étude porte sur les expériences quotidiennes du racisme chez les immigrants chinois au Canada. Les données proviennent de neuf groupes de discussion virtuels composés de 48 participants de sexe et d'âge divers, qui ont eu lieu en décembre 2021 et en janvier 2022. Nous avons mené notre analyse qualitative en nous inspirant de la conceptualisation du racisme quotidien d'Essed et de la conceptualisation de l'intersectionnalité de Crenshaw. L'analyse thématique et l'analyse de contenu ont montré que le racisme à l'égard des immigrants chinois est plus répandu qu'on ne le pensait. Les trois quarts des participants ont révélé des cas de racisme dans divers contextes, notamment sur les lieux de travail, dans les écoles, les magasins, les aéroports, les banques, les hôtels, les restaurants, les rues et en virtuel. Certains groupes de genre et d'âge ont signalé plus d'incidents que d'autres. Les jeunes hommes, en moyenne, en ont subi le plus grand nombre, suivis par les jeunes femmes et les femmes d'âge moyen, tandis que les hommes plus âgés en ont signalé le moins. Les types d'incidents racistes varient également en fonction de l'âge et du genre. Les jeunes femmes et d'âge moyen ont été confrontées à des remarques intimes indécentes et à du racisme lié à l'inégalité du travail entre les sexes, tandis que les adultes plus âgés, hommes et femmes, ont été confrontés à du racisme lié à la prestation de services. Les jeunes hommes ont fait état de racisme virtuel. Certains participants plus âgés n'étaient pas conscients de la nature raciste de leurs rencontres, et les hommes d'âge moyen et plus âgés avaient tendance à nier avoir vécu des rencontres racistes. Compte tenu de ces résultats, nous postulons qu'il est nécessaire de sensibiliser, de mettre en place des initiatives d'autonomisation et d'adopter des approches intersectionnelles pour aborder et combattre le racisme à l'égard des immigrants chinois au Canada.

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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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.502
Threshold uncertainty score0.622

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.353
Teacher spread0.295 · 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 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

Citations3
Published2024
Admission routes2
Has abstractyes

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