Shameless comedy: investigating shame as an exposure effect of contemporary sexist and feminist rape jokes
Bibliographic record
Abstract
This article adds to the conversation of controversial feminist humour by moving away from debates as to whether rape jokes can be funny or feminist and instead examining how they may impact feminist women and female sexual assault survivors. Beginning with a brief discussion of shame’s characteristics and uses, this work investigates various critical status difference factors including the kind of rape joke (sexist or feminist), the gender of the comedian, the composition of the audience, the social setting, and the level of trust, to determine how rape jokes create or alieve shame in female feminist audience members. By studying contemporary rape jokes from comedians including Daniel Tosh, Dave Chappelle, Jim Jefferies, Wanda Sykes, Amy Schumer, and Heather Jordan Ross, through an affective lens, this research shows that whereas sexist rape jokes told by male comedians to a mostly male audience may force women to experience shame (whether intentionally or unintentionally), feminist rape jokes told by female comedians are more likely to eliminate or prevent shame.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".