Variations En Genre et Acceptabilité (ELIPSS 2021)
Bibliographic record
Abstract
Le but du projet est de voir dans quelle mesure l’écriture inclusive est tolérée, acceptée ou rejetée dans l’usage courant, notamment en fonction de l’appartenance politique et/ou sociologique. Nous proposons pour cela de tester un certain nombre de formats possibles, la dénomination générique d’ « écriture inclusive » incluant en réalité de nombreuses possibilités (par exemple étonné.e, acteur.rice, M/Mme/Mix – certaines formes rencontrant déjà une résistance y compris dans les milieux favorables à l’écriture inclusive, comme étonnéE, étonné(e)). Notre hypothèse est que l’écriture inclusive commence à entrer dans les moeurs mais reste polarisante : bien tolérée dans certains milieux, elle l’est moins dans d’autres. The aim of the project is to see the extent to which inclusive writing is tolerated, accepted or rejected in standard usage, in particular with respect to political and/or sociological identity. To do this, we propose to test a number of possible formats, since the generic term “inclusive writing” in reality covers numerous possibilities (for example actor/actress, Mr/Mrs/Ms), and some forms are already meeting resistance even in circles that are favourable to inclusive writing, notably in France variations on the gendering of adjectives. Our hypothesis is that inclusive writing is beginning to become accepted practice but in a polarising way, being better accepted in some circles than others.
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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 | 0.016 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.018 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.061 | 0.029 |
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".