Public pedagogy on sexual violence: A feminist discourse analysis of YouTube vlogs after #MeToo
Bibliographic record
Abstract
In this article, we report findings from a feminist discourse analysis of YouTube vlogs in which women and girls discuss and narrate their experiences of sexual violence. The analysis yielded three discourses and three counterdiscourses: the refusal discourse and the complicating consent counterdiscourse; the deviant perpetrator discourse and the community problem counterdiscourse; and the not that bad discourse and truth telling counterdiscourse. Our findings indicate that the YouTube vloggers simultaneously reproduce and resist dominant sexual violence discourses; they use both dominant and counterdiscourses to understand, situate, and make sense of their experiences of sexual violence. Counterdiscourses were constituted when vloggers resisted dominant discourses by pointing out their inconsistencies and fundamental flaws and presented alternative patterns of meaning. The #MeToo movement and YouTube’s nature as a narrative platform allowed the women and girls in our study to locate their stories of sexual violence within broader contexts and connect them to a continuum of experiences and a complex cultural problem. In a post-#MeToo world, the vloggers’ narratives evidenced their development of a digital networked feminist consciousness. Situated within feminist understandings of sexual violence and prevention education, as well as the emerging research on the #MeToo movement, this study contributes to the literature on public sexual violence pedagogy.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".