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Record W3080958071 · doi:10.1080/24694452.2020.1782169

Blood and Borders: Geographies of Social Reproduction in Ciudad Juárez–El Paso

2020· article· en· W3080958071 on OpenAlexaff
Nina Ebner, Kelsey Johnson

Bibliographic record

VenueAnnals of the American Association of Geographers · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicSex work and related issues
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsReproductionSocial reproductionDevaluationEthnographyGeopoliticsDonationWork (physics)Political scienceSociologyEconomic growthEconomyEconomicsPoliticsLawSocial capital

Abstract

fetched live from OpenAlex

Each week, thousands of Mexican nationals living in northern Mexican border cities cross the border into the United States with nonimmigrant visas to “donate” blood plasma at commercial collection centers in exchange for a prepaid Visa gift card (valued at up to US$50). For these individuals, many of whom work on maquiladora assembly lines, a single donation can nearly double their weekly wages. If the ability to keep wages low remains a key means of leveraging the border’s competitiveness in the global economy, we argue that the devaluation of maquiladora labor in fact relies on the capacity of communities and households to increasingly absorb the hidden costs of social reproduction. Grounded in two years of ethnographic research in Ciudad Juárez–El Paso, this article argues that plasma donation is an increasingly vital strategy through which Mexican households meet the costs of social reproduction. Further, participation in the cross-border plasma economy is inseparable from institutions and frameworks that govern border crossing. By following the movement of Mexican blood plasma across the border, it becomes possible to understand how the border itself—as a material barrier and geopolitical project—shapes collective capacities for social reproduction.

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)0.001
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 score0.040
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
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 head0.019
GPT teacher head0.322
Teacher spread0.303 · 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

Citations16
Published2020
Admission routes1
Has abstractyes

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