"Les féminazis c’est les pires" : autopsie langagière et enjeux sociaux d’une injure antiféministe
Bibliographic record
Abstract
L’objectif de cet article est de proposer une étude de la circulation du mot féminazi et \ndes représentations qu’il convoque, et ce, au moyen d’une étude de corpus en ligne. Constitué de \ntextes médiatiques et de commentaires issus des réseaux sociaux, ce corpus permet d’obtenir une \ncartographie complète et variée afin de mieux comprendre les contextes d’utilisation de féminazi. \nAdoptant une approche croisant analyse de discours et humanités numériques, ce travail sur la violence \nlangagière s’appuie sur la statistique textuelle, l’énonciation et l’argumentation, et est enrichi \npar l’introduction d’un corpus de mèmes, signes graphiques et textuels utilisés et produits en ligne \nafin de proposer des commentaires sur des sujets de société.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".