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Record W1933510338 · doi:10.14288/acme.v5i2.758

Bazaar Stories of Gender, Sexuality and Imperial Spaces in Gilgit, Northern Pakistan

2015· article· en· W1933510338 on OpenAlexaff
Nancy Cook

Bibliographic record

VenueOpen Collections · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicSouth Asian Studies and Conflicts
Canadian institutionsBrock University
Fundersnot available
KeywordsHuman sexualityGender studiesLustSociologyContext (archaeology)Moral panicIndigenousColonialismNegotiationPolitical scienceHistoryPsychologyCriminologySocial scienceLaw

Abstract

fetched live from OpenAlex

This paper provides a material and spatial analysis of processes of sexual imperialism in contemporary northern Pakistan. I interrogate Western women development workers’ experiences of sexual vulnerability in Gilgit, and argue that their representational practices and spatial negotiations are ambivalently organised by a discourse of racialised sexuality that emerged largely in the European era of high imperialism in the context of Western imperial relations and lingers into the ‘colonial present’. This discourse evokes a vaguely articulated moral panic about ‘lascivious’ indigenous men who lust after white women. Western women cope with sexual threat by scrutinising Gilgiti men’s behaviours, regulating social interactions with them, avoiding sexualised local space, and arranging their private spaces to exclude threatening men. Eroticist and racist discourses about Other men that are circulated through these efforts to cope with sexual danger reinforce established social, sexual, and spatial boundaries, which keep imperial hierarchies between Gilgiti men and Western women intact.

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)0.001
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: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0180.021
Scholarly communication0.0040.003
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.082
GPT teacher head0.371
Teacher spread0.289 · 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

Citations7
Published2015
Admission routes1
Has abstractyes

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