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Record W3038574557 · doi:10.7202/1069925ar

Partager la production des connaissances en violence conjugale

2020· article· fr· W3038574557 on OpenAlexaffvenue
Catherine Flynn, Josiane Maheu, Pénélope Couturier, Louise Lafortune, Kathy Mathieu, Geneviève Lessard, Louise Hamelin‐Brabant, Charlotte Gagnon, Marie‐Marthe Cousineau

Bibliographic record

VenueNouvelles pratiques sociales · 2020
Typearticle
Languagefr
FieldSocial Sciences
TopicEducation, sociology, and vocational training
Canadian institutionsUniversité du Québec à ChicoutimiRegroupement des Maisons pour Femmes Victimes de Violence ConjugaleUniversité de MontréalUniversité LavalTable Carrefour Violence Conjugale Québec MétroUniversity of Ottawa
Fundersnot available
KeywordsPolitical scienceHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

Le présent article propose un retour sur la démarche de recherche, expérimentée dans le cadre d’un projet faisant partie de la programmation de TRAJETVI. Ce vaste projet soutient depuis 2013 différentes recherches menées en partenariat sur les violences faites aux femmes vécues en contexte conjugal. La perspective féministe intersectionnelle dans laquelle ce projet inscrit son approche implique de s’attarder aux différents rapports de pouvoir impliqués dans les processus de production du savoir. Les résultats obtenus réitèrent l’importance de mieux soutenir les partenaires des milieux de pratique, qui portent souvent seul.es la responsabilité du projet au sein de leur organisme, de se doter d’un échéancier réaliste ainsi que d’un plan de mobilisation des connaissances qui permet l’appropriation des résultats par toutes les parties impliquées.

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)0.033
metaresearch head score (Gemma)0.047
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.175

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0330.047
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0080.014
Scholarly communication0.0120.006
Open science0.0020.014
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0120.001

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 head0.314
GPT teacher head0.457
Teacher spread0.143 · 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
Published2020
Admission routes2
Has abstractyes

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