What's in the Box? Punishment and Insanity in the Canadian Jury Deliberation Room
Bibliographic record
Abstract
There is a longstanding culture of hostility toward the insanity defence in Canada, where jurors may be hesitant to find defendants Not Criminally Responsible on Account of Mental Disorder (NCRMD) in a criminal trial. While this hesitancy is in part attributable to misinformation about the defence, it might also be seated in moral intuitions about fair punishment. Researchers have made gains in understanding individual juror decision-making in insanity cases, but the jury deliberation room remains a black box. In this mixed-methods study, Canadian jury eligible participants (N = 83) completed attitudinal measures, read a fictional murder case involving a claim of NCRMD, then took part in 45-minute deliberation sessions. Study 1 examined the relationship between punishment orientation, insanity defence attitudes, and individual verdict decisions, and addressed the utility of a hypothetical self-exclusion question (i.e., Challenge for Cause). The Challenge for Cause question was not a useful gauge of individual verdict decisions. However, punishment prone orientations were correlated with higher confidence in a guilty verdict. In Study 2 two independent coders were trained to assess each individual utterance for the presence or absence of categories listed in a codebook that was created a-priori, which yielded fair inter-rater reliability. Hierarchical linear modeling analyses showed that punishment-prone mock jurors were less likely to defer to the authority of the psychiatrist testifying for the Defence. Those who were more punishment-prone also had a lower frequency of Defence position-taking utterances. Finally, in Study 3, a qualitative description of key-word flagged utterances (using the Moral Foundations Dictionary) demonstrated that mock jurors relied on moral intuitions about authority, harm, and fairness in justifying their positions. Study 3 also revealed five general categories with respect to what would happen to the defendant after the trial: the effectiveness of prisons, the conditions of prisons, the jury's duty in considering punishment, desires for rehabilitation, and desires for incapacitation. Overall, findings imply that mock jurors' decisions stem partially from moral conceptualizations of insanity rather than from evidence alone.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".