How do we speak about sexual assaults?
Bibliographic record
Abstract
This paper analyzes the written French media coverage of four cases of public denunciations of sexual assault that occurred during #MeToo movements and that involve public personalities, namely Julien Lacroix, Maripier Morin, Gilbert Rozon, and Éric Salvail. Using a Critical Discourse Analysis approach (Fairclough, 1995; van Dijk, 1988), we consider linguistic features that have been analyzed previously in a mostly English body of research on media discourse surrounding sexual assault cases (e.g., Clark, 1992; Henley et al., 1995; Tranchese, 2023), such as grammatical voice and lexical choices. We use data from 526 articles retrieved from three influential newspapers in Québec, namely La Presse, Le Devoir, and Journal de Montréal. We observe that these linguistic resources tend to reduce the perceived responsibility of the perpetrators by casting them as victims of the public denunciations.
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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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".