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Record W2997521302

The invisibilization of sexism in research on homophobic and transphobic violence in schools in North America

2019· article· en· W2997521302 on OpenAlexaboutno aff
Line Chamberland

Bibliographic record

VenueCahiers du Genre · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicGender Roles and Identity Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyHuman sexualityConformitySocial psychologySexual violenceInterpersonal violenceAffect (linguistics)Interpersonal relationshipGender violenceInterpersonal communicationGender studiesDevelopmental psychologyPoison controlSuicide preventionCriminologySociologyMedicine
DOInot available

Abstract

fetched live from OpenAlex

This article provides a critical reading of three studies conducted respectively in English-speaking Canada, Quebec and the United States, on the school environment and interpersonal violence between peers based on non-conformity with sexual and gender norms in schools. It examines the extent of these forms of violence, which can affect all students, as well as the relatively greater victimization of trans or non-cisgendered youth and students whose gender expression is not in line with gender norms. However, these studies overlook the analysis of sexist violence or violence experienced as girls, including by non-heterosexual girls, as well as the inquiry on the perpetrators of interpersonal violence. While acknowledging the intertwined and systemic nature of sexuality- and gender-related violence, these studies fail to address the asymmetry between girls and boys and the role of interpersonal violence in the reproduction of social gender relations.

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)0.007
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.993
Threshold uncertainty score0.691

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.004
Science and technology studies0.0170.030
Scholarly communication0.0060.004
Open science0.0010.005
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.040
GPT teacher head0.340
Teacher spread0.300 · 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.

Study designQualitative
DomainMethods
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

Citations0
Published2019
Admission routes1
Has abstractyes

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