The use of neuroscientific evidence in Canadian criminal proceedings
Bibliographic record
Abstract
This article addresses the question of how neuroscientific evidence is currently used in the Canadian criminal justice system, with a view to identifying the main contexts in which this evidence is raised, as well as to discern the impact of this evidence on judgements of responsibility, dangerousness, and treatability. The most general Canadian legal database was searched for cases in the five-year period between 2008 and 2012 in which neuroscientific evidence related to the responsibility and recidivism risk of criminal offenders was considered. Canadian courts consider neuroscientific evidence of many types, particularly evidence of prenatal alcohol exposure, traumatic brain injury, and neuropsychological testing. The majority of the cases are sentencing decisions, which is useful given that it offers an opportunity to observe how judges wrestle with the tension that evidence of diminished capacity due to brain damage tends to reduce moral blameworthiness, while it also tends to increase perceptions of risk and dangerousness. This so-called double-edged sword of the biological explanation of criminal behavior was reflected in this study, and raises questions about whether and when the pursuit of such evidence is advisable from the defense perspective.
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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 | 0.022 | 0.144 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.021 | 0.015 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".