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Record W4320729584 · doi:10.1177/13634607231157071

<i>Hijra,</i> trans, and the grids of “passing”

2023· article· en· W4320729584 on OpenAlexaff
Salman Hussain

Bibliographic record

VenueSexualities · 2023
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsYork University
FundersSocial Science Research Council
KeywordsCitizenshipWelfare stateSociologyBody politicContext (archaeology)PoliticsPolitical scienceLawGender studiesHistory

Abstract

fetched live from OpenAlex

This paper examines the contestation about khwajasara corporeality—legal, medical and activist claims about the khwajasara body—and how it has been subjected to state projects of welfare and citizenship in South Asia. The khwajasara/hijra body was a suspicious and a transgressive body for the colonial state, but it has become a target of legal and medical forms of knowledge with the transformation of the “transgender” as a new subject of citizenship in South Asia. Based on ethnographic fieldwork in the khwajasara community in Pakistan, this paper examines how new “grid[s] of intelligibility” (Butler, 2001: 629) informed by legal and medical forms of expertise mediate rights claims as well as fuel social imaginary about khwajasaras—making the body of khwajasara legible as a Gender X citizen while also providing a new political context to which khwajasaras diversely respond. My analysis suggests that the new “citational apparatus” (Mitra, 2020: 111) concerning khwajasara corporeality makes khwajasaras “deserving” subjects of state welfare and protection, but only through introducing new forms of welfare surveillance, mediated by legal and medical experts. The paper suggests that as an object of state intervention as well as a means to elude legibility and demand equality and rights, the body remains central to governmental projects of welfare, governance and citizenship.

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)0.003
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.052
Scholarly communication0.0100.007
Open science0.0010.007
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.051
GPT teacher head0.377
Teacher spread0.326 · 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

Citations18
Published2023
Admission routes1
Has abstractyes

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