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Record W4391168106 · doi:10.7202/1108492ar

La diversité de genre s’invite au Musée de la civilisation de Québec (note de recherche)

2024· article· fr· W4391168106 on OpenAlexaffvenueabout
Karine Geoffrion, M. Drouin, Amélia Fiset, Kévin Lavoie, Caroline Lantagne

Bibliographic record

VenueAnthropologie et Sociétés · 2024
Typearticle
Languagefr
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsMusée de la CivilisationUniversité Laval
Fundersnot available
KeywordsCivilizationArtHumanitiesPhilosophyHistoryArchaeology

Abstract

fetched live from OpenAlex

Les musées ont un rôle important à jouer dans la diffusion et la légitimation de représentations de la diversité sociale auprès d’un large public bien souvent néophyte, ce qui contribue à structurer les rapports qu’entretient la population majoritaire avec des populations souvent minorisées. Cette note de recherche porte sur le processus d’élaboration d’une exposition sur la diversité de genre au Musée de la civilisation de Québec débuté en 2021, à partir des documents de travail du comité de spécialistes, de l’expérience de deux universitaires trans ayant travaillé à la scénarisation, et de discussions avec la chargée du projet. L’objectif de cet article est de situer cette expérience collaborative dans le cadre des développements récents dans le domaine de la diversité du genre dans la société québécoise au niveau politique et institutionnel, et des politiques de la représentation des personnes et communautés LGBTQ+ dans le monde muséal.

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)0.003
metaresearch head score (Gemma)0.006
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 score0.068
Threshold uncertainty score0.493

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0170.010
Scholarly communication0.0080.003
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0130.001

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 head0.248
GPT teacher head0.549
Teacher spread0.301 · 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

Citations0
Published2024
Admission routes3
Has abstractyes

Explore more

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