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Record W2891877879 · doi:10.7202/1051116ar

Le meurtre du partenaire intime chez les femmes au Canada selon qu’elles sont Autochtones ou non-Autochtones

2018· article· fr· W2891877879 on OpenAlexaffvenueabout
Mélanie Girard, Simon Laflamme

Bibliographic record

VenueNouvelles perspectives en sciences sociales · 2018
Typearticle
Languagefr
FieldSocial Sciences
TopicHomicide, Infanticide, and Child Abuse
Canadian institutionsLaurentian UniversityUniversité de Hearst
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

Au Canada, en 2015, le taux de femmes accusées d’homicide était 31 fois plus élevé chez les Autochtones que chez les non-Autochtones. Cette statistique se répercute dans le meurtre du partenaire intime. Dans une étude récente, notre échantillon canadien de femmes maricides était composé à 55 % d’Autochtones. Cette surproportion nous a conduits à nous demander si les femmes autochtones, dans leur rapport au meurtre du partenaire intime, se distinguent des non-autochtones. Nos analyses reposent sur des documents décisionnels de la Commission des libérations conditionnelles et sur des transcriptions des audiences de meurtrières devant la Commission. Ces analyses portent sur des données quantitatives et textuelles. Elles montrent que ces femmes, sur plusieurs plans, ne sont pas différenciables en fonction de l’ethnie; elles révèlent que, lorsque l’ethnie entre en jeu comme facteur de dissimilitude, c’est pour mettre en relief la marginalité d’un vécu amérindien, notamment dans le cadre des réserves.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.130

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0080.003
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.041
GPT teacher head0.314
Teacher spread0.273 · 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 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

Citations0
Published2018
Admission routes3
Has abstractyes

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Same venueNouvelles perspectives en sciences socialesSame topicHomicide, Infanticide, and Child AbuseFrench-language works237,207