Mad and/or bad? Jurors' attitudes towards women and men who plead insanity
Bibliographic record
Abstract
Women are more likely to be perceived as having a mental disorder than men are (McGlynn, Megas, & Benson, 1976).Accordingly, legal decision-makers are more likely to attribute a woman offender's actions to mental illness in comparison to offenders who are men in insanity trials (see Yourstone, Lindholm, & Svenson, 2008).The purpose of this dissertation was to examine mock jury deliberations in a fabricated Not Criminally Responsible on Account of Mental Disorder case.I first examined the impact of defendant gender on jurors' expressions of stereotype content (warmth and competence words) and affect.I used an exhaustive Stereotype Content domain dictionary to guide my directed quantitative content analysis of mock jurors' group deliberations.I used the Linguistic Inquiry Word Count program (LIWC; see Pennebaker, Francis, & Booth, 2001) to comb deliberation transcripts to examine mock jurors' affect towards the defendant (based on the language they used).Second, I examined how juror gender relates to verdict decisions; third, I examined how juror gender relates to speaking roles in deliberations.Fourth, I conducted a thematic analysis of the deliberations and examined how themes related to defendant and juror gender.Overall, these studies did not find significant differences in jurors' use of stereotype content language or affect for men and women defendants.Moreover, I did not find a significant difference in the deliberation styles of women and men jurors.Through the thematic analysis, I found that jurors were generally focused on the mental health status of the defendant and the legitimacy of the NCRMD plea.The present research is of particular importance in Canada, where there is generally no procedural allowance for psycho-legal scholars' questioning of jurors about their social attitudes (e.g., about women) before the trial and about their deliberations after the trial.As such, this dissertation provides a unique and
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".