Criminal Law: Canadian Law, Indigenous Laws & Critical Perspectives
Bibliographic record
Abstract
Criminal Law: Canadian Law, Indigenous Laws & Critical Perspectives is an innovative open access eBook for Criminal Law & Procedure JD/JID courses. It is also a valuable resource for Criminology and Law & Society courses as well as for students, researchers and the general public. This is the first Canadian open access criminal law casebook, incorporating a wide range of traditional and audio/visual materials such as podcasts and documentary films. It is also notable for being the first to present Indigenous laws alongside Canadian criminal law. Cree law is featured throughout and the editors hope to include more Indigenous laws in future editions. Considered by many to be an emerging core competency for lawyers and other legal professionals, a trauma-informed approach is taken in this eBook. This is reflected in the selection of materials, use of content notes, inclusion of mental health and counselling resources, and substantive materials on trauma-informed lawyering, cultural humility, vicarious trauma, and resilience. Critical perspectives are also included on topics such as criminal law as colonial violence, anti-Black racism, intersectionality, social determinants of justice, victims of crime, wrongful convictions, policing, restorative justice, incarceration and prison abolition, and the criminalization of people who use substances and/or experience homelessness, poverty and mental health issues. Click here for more [From Books]
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.021 | 0.016 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.040 | 0.004 |
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".