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Record W4402674283 · doi:10.3138/cjc-2023-0037

Les applications de rencontres et leurs affordances : comment la violence sexuelle facilitée par la technologie est atténuée et perpétuée chez les hommes ayant des relations sexuelles avec des hommes

2024· article· fr· W4402674283 on OpenAlexaffvenueabout
Christopher Dietzel

Bibliographic record

VenueCanadian Journal of Communication · 2024
Typearticle
Languagefr
FieldPsychology
TopicSexuality, Behavior, and Technology
Canadian institutionsConcordia University
Fundersnot available
KeywordsHumanitiesSociologyArt

Abstract

fetched live from OpenAlex

Contexte : Les personnes peuvent subir des violences sexuelles, mais peu d’études ont exploré les expériences des hommes ayant des relations sexuelles avec d’autres hommes (HARSAH). Cette étude porte sur la violence sexuelle parmi les HARSAH qui utilisent les applications de rencontres au Canada. Analyse : Une analyse thématique d’entretiens avec 25 HARSAH a révélé des types communs de violence sexuelle et a mis en lumière la façon dont les HARSAH utilisent les affordances pour gérer ou réduire les incidents de violence sexuelle facilitée par la technologie. Conclusion et implications : Les HARSAH subissent et perpétuent la violence sexuelle en ligne et en personne, et la technologie peut exacerber ou atténuer ce problème.

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)0.006
metaresearch head score (Gemma)0.023
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.084
Threshold uncertainty score0.167

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.023
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0050.004
Scholarly communication0.0070.004
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0160.002

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 head0.094
GPT teacher head0.367
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 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

Citations1
Published2024
Admission routes3
Has abstractyes

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