Bibliographic record
Abstract
This paper will connect literature on epistemic injustice with literature on victims and perpetrators, to argue that in addition to considering the credibility deficit suffered by many victims, we should also consider the credibility excess accorded to many perpetrators. Epistemic injustice, as discussed by Miranda Fricker, considers ways in which someone might be wronged in their capacity as a knower. Testimonial injustice occurs when there is a credibility deficit as a result of identity-prejudicial stereotypes. However, criticisms of Fricker have pointed out that credibility is part of a more complex system that includes both deficits and excesses. I will use these points to argue that we should look closer at sources of credibility excess in cases of sexual assault. This means that in addition to considering sources of victim blaming by looking at ways in which “ideal” victims are constructed, we also need to consider ways in which “ideal” perpetrators are constructed.
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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 | 0.002 | 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 | 0.002 | 0.007 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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; both teacher heads agree on what is shown here.
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".