‘I wanna kill my rapist’: Margaret Cho’s #12DaysofRage campaign as promotional digital activism
Bibliographic record
Abstract
On November 1, 2015, comedian Margaret Cho announced a two-part campaign inspired by her history as a sexual-abuse survivor, to promote her new music video ‘I Wanna Kill My Rapist’. This included the creation of the hashtag #12DaysofRage. In this article, I explore how Cho used her status as a celebrity to circulate #12DaysofRage which acted as a discursive intervention in rape culture. I used content analysis and thematic analysis to identify themes in the archive of 2401 tweets I collected. I also performed a feminist discourse analysis on both the tweets and news coverage of the campaign to situate the hashtag within its historical, social, and political context. I argue that Cho performed what I call ‘promotional activism’, a subsection of celebrity activism where a celebrity promotes a cause as part of the promotion of a particular project or product. Cho’s choice to centre herself in the campaign made it impossible to separate Cho from the hashtag, preventing #12DaysofRage from greater viral potential, but still acting as a resonant, but ephemeral, gathering point for survivor-focused advocacy.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".