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Record W4206441524 · doi:10.1177/08861099211070914

Gender and Trajectories of Marital Breakdown: Accounts of Chinese Immigrant Women in Canada

2022· article· en· W4206441524 on OpenAlexafffundabout
Yanqiu Zhou, Christina Sinding, Jacqueline Gahagan, Évelyne Micollier

Bibliographic record

VenueAffilia · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsThe King's UniversityMount Saint Vincent UniversityWestern UniversityMcMaster University
FundersCanadian Institutes of Health ResearchRenmin University of China
KeywordsImmigrationGender studiesSociologyQualitative researchValue (mathematics)Qualitative propertySettlement (finance)Demographic economicsPolitical scienceSocial science

Abstract

fetched live from OpenAlex

The relatively sparse literature has documented various challenges international migration poses to martial stability, yet we know little about immigrant women's experiences with marital breakdown. Drawing data from a qualitative study of Chinese economic immigrants to Canada, this article explores women's experiences of navigating the processes of this life circumstance, and of how gender-including their senses of changing gender roles in post-immigration and postmarital contexts-plays out in these trajectories. The results of this exploratory study illustrate the value of transcending dichotomous conceptions of the relationship between gender and migration, and of opening spaces in which to better understand immigrant women's increasingly diversified life trajectories and the range of barriers they encounter along the way. The study also reveals multiple opportunities for social work contributions: tackling systematic barriers to settlement, facilitating social support in the community, and recognizing individuals' diverse trajectory potentials (including the potential for this typically unwelcome event to be integrated as personal growth and transition).

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.565

Codex and Gemma teacher scores by category

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

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 head0.006
GPT teacher head0.230
Teacher spread0.224 · 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

Citations2
Published2022
Admission routes3
Has abstractyes

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