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Record W4200217147 · doi:10.3917/nqf.402.0052

Masculinisme, changement climatique et catastrophes produites par les hommes. Vers une réponse environnementale proféministe

2021· article· fr· W4200217147 on OpenAlexaff
Bob Pease, Ellen Hertz, Lucile Ruault, Laurence Bachmann

Bibliographic record

VenueNouvelles Questions Féministes · 2021
Typearticle
Languagefr
FieldEnvironmental Science
TopicClimate Change and Geoengineering
Canadian institutionsMinistère de l’Emploi et de la Solidarité Sociale (Québec)
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

Cet article explore le caractère genré des causes et des réponses aux événements climatiques dans le monde, en prenant comme toile de fond le changement climatique induit par les humains et l’augmentation des catastrophes naturelles liées au climat. Il soutient les arguments féministes de longue date selon lesquels les politiques environnementales progressistes ne sont pas exemptes de masculinité hégémonique et de masculinisme. Loin de promouvoir les « masculinités écologiques », il soutient que les hommes doivent être prêts à renoncer à leurs privilèges masculins et à découvrir de nouveaux modes de relation entre eux ainsi qu’avec les femmes et avec la nature. Pour s’attaquer aux causes sociales et humaines du changement climatique, il faudra alors remettre en question les modèles de masculinité hégémonique qui promeuvent des formes technocratiques et managériales de contrôle de la nature et refusent de reconnaître la vulnérabilité humaine.

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)0.001
metaresearch head score (Gemma)0.002
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: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.006
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.019
GPT teacher head0.255
Teacher spread0.236 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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