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Record W3181541818 · doi:10.1177/08912432211029395

The Gendered Racialization of Asian Women as Villainous Temptresses

2021· article· en· W3181541818 on OpenAlexaff
Maria Cecilia Hwang, Rhacel Salazar Parreñas

Bibliographic record

VenueGender & Society · 2021
Typearticle
Languageen
FieldPsychology
TopicSexuality, Behavior, and Technology
Canadian institutionsMcGill University
Fundersnot available
KeywordsRacializationGender studiesImmoralityWhite (mutation)NarrativeSociologyCriminologyPatriarchyRace (biology)Political scienceMoralityLaw

Abstract

fetched live from OpenAlex

What explains white male animus against Asian women? We address this question by examining the murders in Atlanta, GA, which reflect a larger global pattern of violence against what are perceived as hypersexualized Asian women. Dominant discourses on these murders promote either a narrative of racial xenophobia or a stance for or against sex work. Neither discourse adequately accounts for the simultaneous racial and gendered determination of Asian women’s experiences. In this commentary, we provide a racial–gender analysis and underscore how the gendered racialization of Asian women as hypersexual can result in their perception as disposable bodies for white male rage. As we explain, hypersexualization implies immorality, which in turn threatens the social order and thereby justifies Asian women’s disposability. This commentary establishes Asian women’s hypersexualization as a century-old view in American society perpetuated in cinema and the law.

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)0.001
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: none
Teacher disagreement score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.011
Scholarly communication0.0050.002
Open science0.0010.002
Research integrity0.0020.002
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 head0.046
GPT teacher head0.345
Teacher spread0.299 · 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

Citations46
Published2021
Admission routes1
Has abstractyes

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