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Record W2767669069 · doi:10.7202/1041701ar

Le crime d’honneur : dans les marges de la hiérarchie de genre

2017· article· fr· W2767669069 on OpenAlexvenueno aff
Aurore Schwab

Bibliographic record

VenueCriminologie · 2017
Typearticle
Languagefr
FieldSocial Sciences
TopicGender, Security, and Conflict
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesArtEthnologySociology

Abstract

fetched live from OpenAlex

La victime du crime d’honneur est généralement une femme qui est soupçonnée de gérer sa chasteté d’une façon divergente de l’avis de son groupe familial (en refusant de se marier, en ayant une relation extraconjugale, etc.). Habituellement, l’auteur du crime d’honneur est masculin et fait partie du même groupe familial que la victime. Une hiérarchie de genre est sous-jacente au crime d’honneur et traverse les échelles de gouvernance (locale, nationale, internationale). Pour le montrer, nous étudions le cas du crime d’honneur subi par Samia Sarwar le 6 avril 1999 au Pakistan et plus particulièrement le débat qui a entouré ce meurtre. D’abord, nous analysons la manière dont la hiérarchie de genre peut marginaliser le groupe social des femmes. Ensuite, nous observons le rôle des femmes non seulement comme complices du crime d’honneur, consolidant par là la structure maritale androcentrique, mais aussi comme complices des rituels amoureux concurrents des mariages. En devenant amantes et médiatrices des histoires d’amour, les femmes déstructurent en effet le système patriarcal et patrilinéaire. Enfin, cet article met en lumière, notamment par l’analyse du discours de la rapporteuse spéciale onusienne sur la violence envers les femmes, la manière dont les (op)positions sur le crime d’honneur et la hiérarchie de genre traversent les échelles de gouvernance.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.487
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.004
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0010.001
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 head0.362
GPT teacher head0.417
Teacher spread0.055 · 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; both teacher heads agree on what is shown here.

Study designObservational
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

Citations2
Published2017
Admission routes1
Has abstractyes

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