Reducing Bias in Forensic Evaluations Describing Sexualized Violence
Bibliographic record
Abstract
Forensic examiners often provide reports for individuals seeking redress in courts or tribunals after experiencing sexualized violence. How sexualized violence is described shapes how both examiner and readers perceive the nature, severity, and impact of what occurred. An examiner’s description may, however, be influenced by unconscious bias due to (a) the behavioral similarity between sexualized violence and consenting sexual relations and (b) false societal beliefs about gender, sexuality, and rape. Such influences may shape perceptions of whether the victim was consenting or coerced, whether the perpetrator’s behavior was transgressive, violent, or premeditated, and the extent to which the victim was harmed. To reduce possible bias, we recommend that examiners routinely (a) use the words “sexualized violence” and “sexual relations” accurately, (b) report adequately complete factual information relating to the plaintiff/defendant relationship, (c) report an adequately complete and factual account of the behaviors, thoughts, and feelings of plaintiff and defendant, and (d) communicate findings and opinion using neutral, behavioral, and direct language. These recommendations should be implemented in the context of current knowledge relevant to forensic assessment of sexualized violence, including trauma-informed practices. These steps will help to ensure that forensic descriptions of sexualized violence are adequately complete, neutral, and fair.
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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 | 0.170 | 0.429 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".