The impact of schemas on decision-making in cases involving allegations of sexual violence
Bibliographic record
Abstract
Victims of intimate partner violence (IPV) face significant barriers to having their complaints believed both when initially reporting their experiences and when giving evidence at trial. This is especially the case when they have been sexually assaulted by their partner. These barriers stem not only from misperceptions about what IPV is, but also due to a mismatch between the features of sexual assault in IPV and stereotypic expectations about what ‘real’ rape is—a violent surprise attack by a stranger in an outside location. We examine the research on schemas about sexual assault more generally and consider the way in which these schemas are structured, the functional purpose of such beliefs and the effect they have on perceptions of credibility and decisions about guilt. We review the published literature and discuss the results of some of the research currently in progress in our lab. In doing so, we propose an approach to counter-act the negative effect of these beliefs on whether victims are blamed and how their evidence is perceived, and the decisions made at various stages of the criminal justice system, such as those made by police and jurors.
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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 | 0.014 | 0.104 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".