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.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".