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Record W4224079179 · doi:10.1177/08912432221089630

Between Women of Color: The New Social Organization of Reproductive Labor

2022· article· en· W4224079179 on OpenAlexaff
Jennifer Nazareno, Cynthia J. Cranford, Lolita Lledo, Valerie G. Damasco, Patricia Roach

Bibliographic record

VenueGender & Society · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCitizenshipWomen of colorGender studiesAgency (philosophy)Welfare stateSociologyState (computer science)Social citizenshipPolitical scienceEconomic growthLawEconomicsRace (biology)Social sciencePolitics

Abstract

fetched live from OpenAlex

In this article, we examine citizenship inequalities in paid reproductive labor. Through an analysis of elder care in Los Angeles, California, based on interviews with Filipina home care agency workers and owners, we delineate citizen divisions made up of two interlocking dimensions. The longstanding U.S. welfare state abdication of responsibility for elder care for its citizens generates a racialized, gendered citizenship division that facilitates another citizenship division between women of color. The outsourcing of elder care by the government to the private sector including small business in the ethnic economy allows Filipina immigrants with legal citizenship to become middle-women minorities who hire undocumented Filipinas to provide care for white, middle-class, older adult women and their families. Through this new social organization of reproductive labor, responsibility is directed away from the state and generating tensions between women of color with different legal statuses. Our findings show how racialized, gendered inequalities are reinforced through this new social organization of reproductive labor but also demonstrate potential for undermining intersecting 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 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.156
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.036
GPT teacher head0.285
Teacher spread0.250 · 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.

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

Citations4
Published2022
Admission routes1
Has abstractyes

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