Bibliographic record
Abstract
This chapter focuses on women’s use of the Twitter hashtag #BeenRapedNeverReported. Using the hashtag, hundreds of girls and women shared the reasons they didn’t report incidents of sexual assault by partners, family members, friends, and acquaintances. We explore how this feminist hashtag developed in response to the public allegations of sexual violence made about then-popular Canadian CBC radio host Jian Ghomeshi, and ultimately moved across the media landscape, producing a robust public discussion about sexual violence and rape culture. Drawing on thematic analysis of #BeenRapedNeverReported tweets and interviews with eight women who contributed to the hashtag, we analyze the “affective solidarity” produced along this hashtag and the ways it created new lived possibilities for feminist identification, experience, organizing, and resistance. We contextualize this analysis within a larger Canadian media culture to position the hashtag as both a discursive and affective intervention into hegemonic public discourse about rape culture and sexual violence.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".