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Record W4285126584 · doi:10.1051/shsconf/202213812003

Étude comparative sur l’usage de l’écriture inclusive dans deux médias écrits français et québécois

2022· article· fr· W4285126584 on OpenAlexaffabout
Nikita Kamblé-Bagal, Anaïs Tatossian

Bibliographic record

VenueSHS Web of Conferences · 2022
Typearticle
Languagefr
FieldSocial Sciences
TopicGender Studies in Language
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsHumanitiesArtPolitical science

Abstract

fetched live from OpenAlex

La présente étude compare l’usage de l’écriture inclusive dans le journal français Le Monde et le journal québécois Le Devoir entre octobre 2017 et septembre 2021. Le corpus est constitué d’un ensemble de 192 articles, soit 96 articles du Monde et 96 articles du Devoir, ce qui équivaut à deux articles par mois par journal. Les phénomènes inclusifs recensés dans le corpus sont les suivants : la féminisation des noms de métiers, l’accord de proximité, les mots épicènes, les doubles flexions et les abréviations. L’analyse des données révèle que les techniques de l’écriture inclusive sont plus répandues dans Le Monde que Le Devoir, dans la mesure où la plupart des techniques sont relevées dans un plus grand nombre d’articles du Monde que du Devoir. Par ailleurs, les analyses statistiques montrent que les occurrences des phénomènes inclusifs augmentent considérablement entre octobre 2017 et septembre 2021 dans les deux journaux. L’écriture inclusive est donc de plus en plus répandue dans les médias écrits.

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)0.006
metaresearch head score (Gemma)0.023
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 score0.384
Threshold uncertainty score0.772

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0090.013
Science and technology studies0.0080.004
Scholarly communication0.0070.003
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.001

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 head0.051
GPT teacher head0.340
Teacher spread0.289 · 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

Citations27
Published2022
Admission routes2
Has abstractyes

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Same venueSHS Web of ConferencesSame topicGender Studies in LanguageFrench-language works237,207