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Record W2793572917 · doi:10.1177/0896920517748499

Becoming Homecare Workers: Chinese Immigrant Women and the Changing Worlds of Work, Care and Unionism

2018· article· en· W2793572917 on OpenAlexafffund
Jennifer Jihye Chun, Cynthia J. Cranford

Bibliographic record

VenueCritical Sociology · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of CanadaU.S. Department of Labor
KeywordsImmigrationEthnic groupChinatownSubsidyCare workSplit labor market theorySociologyOpportunity structuresWork (physics)Political scienceLabour economicsEconomicsLabor relationsPolitics

Abstract

fetched live from OpenAlex

This article examines how the intersectional dynamics of gender, migration, and labor shape the trajectories of immigrant women into home-based elder care and how unions and community organizations mediate its conditions. Our analysis, which uses interviews with In-Home Supportive Services workers in California’s Oakland Chinatown, shows that the growth of publicly-subsidized homecare jobs has created an occupational opportunity for workers who face restrictive labor markets due to declining factory jobs and discriminatory hiring. Workers acknowledge the daily stress of working in low-paid, precarious jobs characterized by high levels of informality and coercive gender and racial-ethnic dynamics but the quasi-public nature of these jobs complicates a facile depiction of it as domestic servitude. Ethnic community organizations and labor unions open up institutional pathways to empowering forms of collective voice. Our findings contribute to the growing effort to understand how the social organization of care work both draws upon and exacerbates existing inequalities.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.085
Threshold uncertainty score0.169

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0130.010
Scholarly communication0.0050.003
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.018
GPT teacher head0.318
Teacher spread0.300 · 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 source (direct Gemma or distilled Codex), 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

Citations27
Published2018
Admission routes2
Has abstractyes

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