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Record W2745815620 · doi:10.7202/1040750ar

Militer par le témoignage public : défis et retombées pour les communautés sexuelles et de genres

2017· article· fr· W2745815620 on OpenAlexaffvenue
Maria Nengeh Mensah, Janik Bastien Charlebois, Olivier Vallerand, Sandra Wesley, Ken Monteith

Bibliographic record

VenueReflets Revue d’intervention sociale et communautaire · 2017
Typearticle
Languagefr
FieldSocial Sciences
TopicEducation, sociology, and vocational training
Canadian institutionsMinistère de l’Emploi et de la Solidarité Sociale (Québec)
Fundersnot available
KeywordsHumanitiesArtPolitical scienceSociology

Abstract

fetched live from OpenAlex

Dans cet article, nous nous penchons sur l’expérience du militantisme par le biais des témoignages de personnes qui luttent pour l’inclusion sociale de leurs communautés sexuelles et de genres. Nous décrivons l’expérience de militantes et militants issus de trois groupes sociaux minorisés en raison de leur sexualité et de leur expression de genre ou du développement de leur corps sexué : les personnes lesbiennes, gaies, bisexuelles, trans, queer et intersexes (LGBTQI), les personnes vivant avec le VIH et les personnes ayant une expérience de travail du sexe — ainsi que leurs intersections. Nous mettons de l’avant une conception politique, sensible et intersectionnelle de la notion de communauté afin de dresser un portrait transversal des expériences de militance par le témoignage tout en relevant les singularités des points de vue qui la composent. En guise de conclusion, nous dégageons les éléments qui permettent d’entrevoir ces militantes et militants et leurs prises de parole publiques comme le prolongement des interventions féministes du siècle dernier et comme lieu de nombreux défis épistémiques et de mobilisation.

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)0.005
metaresearch head score (Gemma)0.008
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 score0.021
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0150.022
Scholarly communication0.0100.007
Open science0.0010.010
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0100.001

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 head0.287
GPT teacher head0.505
Teacher spread0.218 · 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

Citations4
Published2017
Admission routes2
Has abstractyes

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