Undermining Reasonableness: Expert Testimony in a Case Involving a Battered Woman who Kills
Bibliographic record
Abstract
Student participants ( N = 316) viewed a videotaped simulated case involving a woman who had entered a self-defense plea in the shooting death of her abusive husband. As successful claims of self-defense rest on the portrayal of a defendant who has responded reasonably to his/her situation, the implications of various forms of expert testimony in constructing this narrative were examined. Jurors were presented with either expert testimony regarding the battered woman syndrome (BWS), the BWS framed within post-traumatic stress disorder (PTSD) nomenclature, or a no-expert control condition. As the BWS classification may support a stereotypical victim, the degree to which the defendant fit the stereotype in terms of her access to a social support network (family, friends, employment outside of the home) was varied within the expert testimony conditions to reflect either a high or low degree of stereotype fit. Although jury verdicts failed to differ across expert testimony and stereotype fit conditions, perceptions of her credibility and mental stability did. Although affording jurors a framework from which the defendant's experiences as a battered woman may be acknowledged, this portrayal, as advanced within PTSD nomenclature, endorsed a pathological characterization of the defendant. Implications of this discourse for battered women within the context of self-defense are discussed.
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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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".