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Record W2896822160 · doi:10.3917/ls.165.0139

Les appellations des identités de genre non traditionnelles. Une approche lexicologique

2018· article· fr· W2896822160 on OpenAlexaffabout
Mireille Elchacar, Ada Luna Salita

Bibliographic record

VenueLangage et société · 2018
Typearticle
Languagefr
FieldArts and Humanities
TopicLinguistics and Discourse Analysis
Canadian institutionsUniversité de SherbrookeUniversité TÉLUQ
Fundersnot available
KeywordsHumanitiesQueerPolitical scienceArtSociologyGender studies

Abstract

fetched live from OpenAlex

Les appellations des identités de genre non traditionnelles se sont multipliées ces dernières années. L’anglicisme queer se retrouve en français, mais est concurrencé par des formes comme LGBT , allosexuel , altersexuel, trans ou bispirituel . Les groupes concernés tentent d’acquérir du capital symbolique en proposant des appellations englobant davantage d’identités de genre et d’orientations sexuelles. La présente étude est une analyse lexicologique de ces néologismes et des facteurs qui peuvent favoriser ou au contraire freiner leur circulation dans la presse générale au Québec. Les groupes qu’on dénomme par ces appellations ont-ils fait part de préférences ou de commentaires à leur sujet ? Est-ce qu’on sent une préoccupation normative pour certaines appellations, comme l’anglicisme queer ? L’intégration au système lexical du français est-elle une condition nécessaire à la circulation des termes ? L’article tente de dégager lequel de ces facteurs pèse plus que les autres.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.127
Threshold uncertainty score0.252

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.004
Science and technology studies0.0040.007
Scholarly communication0.0070.005
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0150.002

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 head0.095
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 designNot applicable
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

Citations12
Published2018
Admission routes2
Has abstractyes

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