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Record W3135977373 · doi:10.3917/dio.267.0009

Être féministe en 2020 ou Comment faire face au succès ?

2021· article· fr· W3135977373 on OpenAlexaff
Éliane Viennot, Joëlle Wiels

Bibliographic record

VenueDiogène · 2021
Typearticle
Languagefr
FieldMedicine
TopicHistorical and Scientific Studies
Canadian institutionsFrancophone University Association
Fundersnot available
KeywordsHumanitiesPhilosophyArt

Abstract

fetched live from OpenAlex

Rédigé par deux féministes qui avaient vingt ans dans les années 1970, dont l’une est devenue biologiste et l’autre spécialiste des relations de pouvoir entre les sexes, cet article postule que l’humanité est en train de vivre un nouveau tournant dans cette histoire, en ce que la fin de la domination masculine est désormais envisageable. Or si l’égalité femmes-hommes est aujourd’hui plus légitime que jamais, elle suscite toujours des oppositions farouches – alors même que les certitudes établies sur « les deux sexes » vacillent au gré des progrès de la science et de la médecine. Cette nouvelle étape exige de celles et ceux qui veulent voir progresser ce chantier de rompre avec les travers issus du temps de la marginalité, et d’acquérir les connaissances scientifiques et historiques dont le manque pourrait faire le jeu de leurs opposant·es.

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)0.004
metaresearch head score (Gemma)0.004
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.022
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.013
Scholarly communication0.0050.006
Open science0.0010.002
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0140.002

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 head0.029
GPT teacher head0.277
Teacher spread0.249 · 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
GenreCommentary

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
Published2021
Admission routes1
Has abstractyes

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