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Record W2768384443 · doi:10.3138/utlj.2017-0075

The banishment of Isaac: Racial signifiers of gender performance

2018· article· en· W2768384443 on OpenAlexaffvenue
Ido Katri

Bibliographic record

VenueUniversity of Toronto Law Journal · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicJewish and Middle Eastern Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPerformative utteranceHarmEthnic groupTransgenderSociologyReading (process)Race (biology)Construct (python library)GazeGender studiesLawPolitical sciencePsychologyLinguistics

Abstract

fetched live from OpenAlex

This article suggests that a performative reading of discrimination cases allows for the recognition of intersectional harms and facilitates a broader systemic account of exclusion from resources and opportunities. Revealing the protected category of sex as a prohibition against discrimination on the basis of gender performance, the article considers how signifiers marked on the gendered body shape the protected categories relating to race and ethnicity. The article suggests that racial/ethnic signifiers and sex/gender performance function reciprocally to construct material realities of exclusion from resources and opportunities. Drawing on the trans position in anti-discrimination, the article offers a nuanced reading of discrimination suffered by Jews of Arab decent, the Mizrahim, under Israeli law. It shows that courts could address systemic aspects of individual claims by looking for the intersecting differentiating logics at the root of private discrimination. The article argues that protected legal categories do not reflect pre-legal truths but, rather, constitute them; that when the law prohibits discrimination on the basis of sex, it prohibits discrimination on the basis of gender performance and that gendered performance is always already marked by racial signifiers. Thus, by turning the legal gaze to the racial signifiers of gender performance, intersecting harm can be better accounted for.

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)0.000
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.487
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.021
GPT teacher head0.246
Teacher spread0.224 · 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 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

Citations5
Published2018
Admission routes2
Has abstractyes

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