Gender, non-binary gender identity - a linguistic and sociological approach of francophone speakers
Bibliographic record
Abstract
The thesis focuses on the issue of gender, in particular its non-binary conception, and investigates how the French language and Francophone society approach this area. The core chapters examine the multifaceted aspects of gender, incorporating sociocultural perspectives and linguistic approaches. In the first chapter, the thesis briefly scrutinizes the philosophy of language and linguistic culture in general. The second chapter explains pivotal concepts related to gender and introduces diverse cultural conceptions of gender. The third chapter describes key linguistic phenomena that pose challenges to achieving neutrality within the French language. The core of the linguistic part of the thesis is the fourth chapter, with the so-called écriture inclusive, inclusive writing, which at many points offers a solution to linguistic equality. The official forms of this approach in France and Canada are presented and its guiding principles are described. In addition, the thesis incorporates a proposal for a gender-neutral solution for the French language put forth by Florence Ashley, a legal and health activist specializing in transgender issues in Canada. The last chapter, chapter five, outlines reactions to the inclusive and gender-neutral form of the French language. The controversial debates that arose...
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.025 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".