Bibliographic record
Abstract
Abstract There is an undeniable tendency to dismiss women's sexual assault allegations out of hand. However, this tendency is not monolithic – allegations that black men have raped white women are often met with deadly seriousness. I argue that contemporary rape culture is characterized by the interplay between rape myths that minimize rape, and myths that catastrophize rape. Together, these two sets of rape myths distort the epistemic resources that people use when assessing rape allegations. These distortions result in the unjust exoneration of people we cannot conceive of as monstrous, while making it too easy to believe that some marginalized people could be rapists. I also argue that rape myths enable a novel kind of epistemic injustice. This injustice concerns how our assessments of trustworthiness and our assessments of plausibility interact. I argue that rape myths can result in runaway credibility deflations that can explain both why people fail to believe most women, and also why people may unjustly believe false allegations that white women have been raped by black men.
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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 | 0.022 | 0.106 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.005 | 0.066 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".