La rédaction inclusive en droit: Pourquoi les objections ratent-elles la cible?
Bibliographic record
Abstract
These are interesting times for the French language. Inclusive writing (or feminization) is more and more widespread, and the legal community is no exception: lawyers, notaries and judges of all stripes are carving out greater space for women and non-binary people in language. There are, however, pockets of resistance. Many people wonder whether inclusive language is truly appropriate in legal texts or if it should be confined to more informal contexts. In this article, we address these questions and take apart eight common objections to non-sexist legal writing: 1) Grammatical gender has nothing to do with a person’s gender 2) Grammar rules have nothing to do with patriarchy3) Inclusive writing is a superficial undertaking4) The feminine form bogs down writing5) Judges do not use inclusive or gender-neutral writing6) Feminization introduces errors in French7) Non-sexist writing is too imprecise when it comes to the law8) Feminization reinforces the binarity and sexism of the French language We will see that these objections are really myths founded on shaky ground.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.011 | 0.023 |
| Scholarly communication | 0.018 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".