The role of discrete emotional reactions to child sexual abuse (CSA) testimony in mock juror decision-making
Bibliographic record
Abstract
Child sexual abuse (CSA) cases often involve graphic descriptions of abuse. Jurors may experience emotional reactions to this type of evidence, which may impact decision making. Two studies were conducted to understand mock jurors' emotions in response to children's testimony about alleged CSA, how emotions relate to moral outrage, objective verdict decisions, sentence recommendations, and witness evaluations. Jury-eligible participants (Study 1 N=143, Study 2 N=169) reported their emotions (i.e., anger, sadness, and disgust) before and after exposure to child testimony in a CSA case, and then provided case decisions (i.e., verdict decision, verdict confidence, sentencing length) and reported moral outrage. Participants experienced increases in all emotions from pre to post transcript, with the greatest increase in disgust. Moral outrage mediated the relationship between disgust and sentencing recommendations (Study 1 & 2), and between disgust and credibility ratings (Study 2). These studies reveal that CSA cases elicit negative emotions in jurors and emotions predict more punitive decision-making, posing a concern for objectivity.
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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 | 0.002 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".