Youth Justice and Cognitive Diversity: A Review of Law and Neurodiversity: Youth with Autism and the Juvenile Justice Systems in Canada and the United States, Dana Lee Baker, Laurie A. Drapela & Whitney Littlefield (Vancouver: UBC Press, 2020)
Bibliographic record
Abstract
The book Law and Neurodiversity: Youth with Autism and the Juvenile Justice Systems in Canada and the United States explores the complex relationship between juvenile justice, disability, and intersectionality (or multilayer discrimination). 1 Its highly sensitive analysis offers a comparative legal and public policy perspective between the United States and Canada with a primary objective: closing the gap between the theory and practice of juvenile criminal justice in relation to disability rights and neurodevelopmental conditions such autism and dyslexia.Dana Lee Baker, Laurie Drapela, and Whitney Littlefield's transdisciplinary research tackles a compelling issue: when multiple variables such as justice, youth, disability, and intersectionality are involved, balancing the social-collective right to security -inherently entrenched in the criminal justice system -with the rights and needs of highly vulnerable and underprivileged juveniles becomes extremely challenging.For decades, in Europe and North America, this balance appeared to favour repressive criminal justice.Although there are some relatively recent reforms in most Western countries, the widespread lack of implementing rehabilitative and restorative forms of justice is cause for concern.Social and bench sciences have long established that there is a higher prevalence of criminal justice involvement for specific classes of individuals in both adult and juvenile inmate populations.It is unquestionable, especially in the US, that there is an overrepresentation in prisons of ethnic and racial minorities, 2 underserved cognitive-diverse individuals, 3 the underprivileged and those disenfranchised by poverty, as well as people having experienced Adverse Childhood Experiences (ACEs) such as trauma, stigma, abuse, discrimination, and neglect.4 However, advancements in neuroscientific knowledge on brain functioning, cognition, and development mechanisms have sparked debate about long-held criminological assumptions.According to new evidence and theories, dysfunctional and maladaptive behaviours correlate with underserved, undiagnosed, and misunderstood neurodevelopmental and chronic conditions, as well as with the onset of neurodegenerative diseases such as Alzheimer's disease or dementia, and can be responsible for disruptive and 1
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".