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Record W4316687848 · doi:10.1111/cars.12414

Digital Ethnic Enclaves: Mate Preferences and Platform Choices Among Chinese Immigrant Online Daters in Vancouver

2023· article· en· W4316687848 on OpenAlexafffundabout
Manlin Cai, Yue Qian

Bibliographic record

VenueCanadian Review of Sociology/Revue canadienne de sociologie · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicMarriage and Sexual Relationships
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of British Columbia
KeywordsImmigrationEthnic groupGeographyPopulationIntersection (aeronautics)AdvertisingDemographySociologyBusiness

Abstract

fetched live from OpenAlex

In light of the growing racialized immigrant population in Canada and advances in dating technologies, this study examines Chinese immigrants' partner preferences and mate selection processes through the lens of online dating. We draw on in-depth interviews with 31 Chinese immigrants who have used online dating services in Metro Vancouver to search for different-sex partners. Chinese immigrant online daters show strong preferences for dating Chinese. They emphasize permanent residency status and similarity in age at arrival when evaluating potential partners. Given their preferences, Chinese immigrants strategically choose the dating platforms they primarily use. Men exhibit higher selectivity in their preferences and choices of platforms. Notably, platforms catering to Chinese users create "digital ethnic enclaves" where Chinese immigrant daters congregate. The findings illuminate the intersection of race, gender, immigrant status, and age at arrival in shaping divergent experiences of mate selection and immigrant assimilation in the digital era.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.823
Threshold uncertainty score0.893

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.090
GPT teacher head0.336
Teacher spread0.246 · 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

Citations7
Published2023
Admission routes3
Has abstractyes

Explore more

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