French and Canadian Inclusive Language Doctrine and Societal Attitudes
Bibliographic record
Abstract
One of the most important French grammar rules is the rule of superiority: Masculine subjects always trump feminine subjects when there are multiple subjects. Superiority is closely followed by the acceptance that all nouns have a grammatical gender, either masculine or feminine. Since 1984, and over the span of forty years, these rules have been challenged on multiple levels of French society. The research conducted over the course of this thesis focuses on the mentality and reactions of the French people towards inclusive language made up of inclusive writing campaigns, the feminization of traditionally masculine names, career positions, and titles, and the introduction of gender-neutral forms of conjugating and neo-pronouns. The studied responses are be categorized into those of the French government, the Académie Française, as well as those from the Canadian government and the Office québécois de la langue française. Research demonstrates the existence of a clear division between "traditionalist" and progressive values at work within the afore-mentioned levels of French societal attitudes. While official government publications and committees seem to reflect a positive attitude towards the adoption of feminized terms, the lack of support for inclusive writing systems by the government contradicts this. This thesis outlines these responses and reactions, seeking to establish a timeline for the implementation and acceptance of feminized terms and neutralization efforts in both the French and Canadian governments.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.018 | 0.013 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
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".