LINGUISTIC ASPECT OF CONTEMPORARY GENDER CHALLENGES IN THE FRENCH LANGUAGE
Bibliographic record
Abstract
The article discusses the morphological features associated with the formation of feminine words to designate professions, ranks and positions. The change in the social status of a woman – a politician, public figure, government official, professional – in the fields confined to male representatives – requires adequate expression in the language. The need search correct forms that do not violate the traditional structure of the language is felt both by linguists and authorities of the country. Their acceptance or non-acceptance by the language depends on the reaction of the native speaker, the media, representatives of the Internet community. The author reviews the possibilities presented by the French language in the formation of the feminine nouns – suffix formation, epicenes. Issues related to the peculiarities of matching plural nouns are also considered. The article does not only deal with the situation in France, but also with what is being done in this direction in Geneva canton, in the French-speaking community of Belgium, in Quebec. The author found it interesting to dwell on some of the reasons that impede the entry of new forms into modern French. The conclusion contains some observations covering the period of the last two years, made on the basis of viewing media materials.
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 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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".