Bibliographic record
Abstract
Cet article explore les usages des médias socionumériques par des collectifs féministes québécois pour combattre les violences sexuelles. Il analyse précisément les pratiques militantes déployées autour du mot-clic #StopCultudeDuViol dans la foulée de la vague d’agressions dans une résidence de l’Université Laval et de l’affaire Paquet-Sklavounos survenues en octobre 2016. À partir d’une observation ethnographique menée dans les comptes Facebook et Twitter de sept collectifs, suivie d’entretiens compréhensifs avec les porte-paroles désignées des collectifs retenus, nous montrons que les féministes québécoises se sont pleinement appropriées le langage numérique. Images, vidéos et mots-clics habillent et complètent leurs discours militants, leur permettant efficacement d’informer, de venir en aide, de prendre position et de faire événement. Il se produit une performativité des actions en ligne qui reconfigure les formes du militantisme féministe et conduit à l’activation d’un « espace de la cause des femmes » autour de la culture du viol.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".