Celebrity ‘rape-rape’: An analysis of feminist and media definitions of sexual violence
Bibliographic record
Abstract
In 2009 a US-based television programme,The View, discussed the arrest of film director Roman Polanski. Polanski was wanted for six outstanding charges related to the rape of Samantha Gailey in 1977. During this episode of theThe View, Whoopi Goldberg made a controversial statement that Polanksi was not guilty of ‘rape-rape’. This statement along with the long history of Polanski’s avoidance of incarceration, illustrates the ongoing challenges for feminists to confront the trivialisation of sexual coercion and violence. Goldberg’s comments initiated an enthusiastic response on online forums and reinvigorated debates around definitions of rape. In this paper, I analyse online discussions on a feminist blog using discourse analysis (Parker, 2014) and the importance of considering the interrelated concepts of consent/non-consent, pleasure/distress and power in understanding the complexity and diversity of experiences of sexual violence.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".