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Record W3012132350

Quel genre de droit? Autopsie du sexisme dans la langue juridique (Autopsy of Sexism in Legal Language)

2017· article· fr· W3012132350 on OpenAlexaff
Michaël Lessard, Suzanne Zaccour

Bibliographic record

VenueSSRN Electronic Journal · 2017
Typearticle
Languagefr
FieldSocial Sciences
TopicComparative and International Law Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsHumanitiesArtSociologyPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

French Abstract: Diriez-vous d’un tribunal dont on limite la competence qu’on lui coupe les couilles? Diriez-vous d’une enfant violee qu’elle a vecu une « aventure sexuelle »? Diriez-vous de 30 avocates et d’un document qu’« ils » se trouvent dans la salle d’audience? Si vous avez repondu « non » a ces provocations, vous aimerez cet article. Si vous avez repondu « oui », vous en avez besoin. Nous traquons ici le sexisme dans la langue du droit. Effacer les femmes, pathologiser les meres, banaliser les violences: tels sont quelques-uns des effets discriminatoires de ce sexisme langagier que nous entreprenons de detailler sous toutes ses coutures. L’analyse du sexisme langagier doit devenir un champ d’etude en bonne et due forme. A cette fin, nous offrons une nomenclature des sexismes jurilinguistiques (lexical, grammatical, terminologique…), ainsi que deux nouvelles notions: la feminisation ostentatoire, un feminin marquee a l’oral, et le plafond de verre linguistique, cette obstination a nommer au masculin les femmes occupant de hautes fonctions. Notre etude offre des outils aux juges, avocat·es, notaires, legistes et autres practicien·nes du droit pour demasquer le sexisme cache dans leurs communications et se familiariser avec les nouveaux developpements en matiere de redaction inclusive. English Abstract: Would you say of a court whose jurisdiction was reduced that it was “emasculated”? Would you say of a child who was raped that she enjoyed a “sexual adventure”? Would you say of a high-ranking woman that “he” got the job? If you answered “no” to these provocations, you will enjoy this article. If you answered “yes”, you need to read it. Here we track sexism in the language of the law. Erasing women, pathologizing mothers, normalizing violence: these are but some of the discriminatory effects of the linguistic sexism that we undertake to detail in its every shape and form. The study of linguistic sexism must become a proper area of research. To this end, we offer a nomenclature of jurilinguistic sexisms (lexical, grammatical, terminological), as well as two new notions: ostentatious feminines and the linguistic glass ceiling. The former qualifies feminine forms that are significantly different from the masculine. The latter refers to some people’s stubborn designation of women in power in the masculine form in French. Our study offers tools for judges, lawyers, notaries, legists, and other legal practitioners to unmask the sexism hidden in their communications and familiarize themselves with new developments in inclusive writing.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.346
Threshold uncertainty score0.965

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.313
Teacher spread0.302 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2017
Admission routes1
Has abstractyes

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