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Record W2751079242 · doi:10.7202/1040997ar

L’INTERVENTION SOCIALE AUPRÈS DES HOMMES GAIS

2017· article· fr· W2751079242 on OpenAlexaffvenue
David Buetti, Lilian Negura, Marie-Hélène Gervais

Bibliographic record

VenueCanadian social work review · 2017
Typearticle
Languagefr
FieldSocial Sciences
TopicEducation, sociology, and vocational training
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsHumanitiesSociologyArt

Abstract

fetched live from OpenAlex

Cet article présente les résultats d’une étude, de nature qualitative et exploratoire, effectuée dans le cadre théorique des représentations sociales. Il a pour objectifs de : 1) documenter, au moyen d’images, la représentation qu’ont les hommes gais de l’homosexualité masculine en société et 2) comprendre comment ces images façonnent les orientations et les modalités d’intervention souhaitées par les hommes gais. Dix entretiens semi-dirigés ont été menés auprès d’hommes s’identifiant comme gais, âgés de 22 à 54 ans, principalement caucasiens, de différents états civils et niveaux de scolarité. L’analyse inductive de contenu a mis en relief la présence de cinq images de l’homosexualité masculine produites par le processus représentationnel d’objectivation : a) la normalité; b) la flamboyance; c) la vulnérabilité; d) la déviance; e) l’hypersexualisation. Les interventions souhaitées par les répondants visent surtout des changements dans le sens du renforcement de la seule image perçue favorablement, celle de la normalité. L’article suggère que les interventions doivent plutôt viser le changement des représentations sociales à l’origine des images négatives de l’homosexualité masculine afin de cultiver le respect envers tous les hommes gais, et non pas seulement envers ceux qui se conforment au modèle hétéronormatif.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.746
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0110.005
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.320
GPT teacher head0.476
Teacher spread0.156 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreReview

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

Citations2
Published2017
Admission routes2
Has abstractyes

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