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Record W4226283203 · doi:10.7202/1087834ar

La violence sexuelle, un problème sociétal majeur mondial, une situation unique : le Japon

2022· article· fr· W4226283203 on OpenAlexaffvenueabout
Chantal Pioch, Ikuo Aizawa

Bibliographic record

VenueEnjeux et société Approches transdisciplinaires · 2022
Typearticle
Languagefr
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

La sécurité est un facteur important de qualité de vie et de bien-être personnel et communautaire. Or, depuis 2002, l’Organisation mondiale de la santé (OMS) a identifié la violence sexuelle comme un problème mondial de santé publique grave. Cette forme de violence est un problème majeur, une violation des droits fondamentaux (World Health Organization [WHO], 2012, 2017), et le Canada n’y échappe pas. Benoit et al. (2015) déclarent d’ailleurs qu’elle est l’un des enjeux actuels les plus pressants en matière de droits de la personne. Toutefois, un pays semble être une exception puisque son taux de violence sexuelle est l’un des plus faibles au monde : le Japon. Celui-ci présente un taux de violence sexuelle 15 fois inférieur à celui du Canada. C’est pourquoi cette étude s’est penchée sur ce phénomène unique en effectuant un portrait comparatif global entre le Japon et le Canada afin de repérer des éléments et des contextes suffisamment contrastants qui pourraient expliquer cette différence.

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)0.003
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 score0.156
Threshold uncertainty score0.310

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0080.010
Scholarly communication0.0050.004
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 head0.028
GPT teacher head0.334
Teacher spread0.307 · 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

Citations1
Published2022
Admission routes3
Has abstractyes

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