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Record W2313676937 · doi:10.1177/0117196816639056

The making of transnational social space: Chinese women managing careers and lives between China and Canada

2016· article· en· W2313676937 on OpenAlexafffundabout
Hongxia Shan, Ashley Pullman

Bibliographic record

VenueAsian and Pacific migration journal · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsWestern UniversityUniversity of British Columbia
FundersAsia Pacific Foundation of Canada
KeywordsChinaGender studiesContext (archaeology)Race (biology)SociologyPower (physics)Field (mathematics)Qualitative researchSpace (punctuation)Class (philosophy)Political scienceEconomic growthGeographySocial scienceLaw

Abstract

fetched live from OpenAlex

China–Canada people flows are increasingly characterized by two-way movement, engendering possibilities and problems, particularly for women juggling careers and lives. Within this context, a qualitative study was conducted to trace the migratory and career trajectories of 15 Chinese migrant women between China and Canada. The study finds that to maximize their career and life spaces, the women endeavored to build and mobilize various forms of capital. Not only did they engage in migratory movement, but some of them also acquired Canadian credentials, moved into entrepreneurship and took up transient jobs. The utility and futility of women’s efforts point to “games” of differentiation emanating across fields, particularly along the lines of gender, race and class that were invoked to produce transnational spaces where existing power relations were simultaneously challenged and reaffirmed. Conceptually, this paper is informed by the concept of transnational social field and gender, race and class analysis.

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)0.001
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 score0.608
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.009
GPT teacher head0.245
Teacher spread0.236 · 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

Citations8
Published2016
Admission routes3
Has abstractyes

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