Preface and Acknowledgments
Bibliographic record
Abstract
Section 718.2(e) of the Criminal Code of Canada is a sentencing provision designed to address the overrepresentation of Aboriginal people in Canadian prisons. 1 This section was most recently considered by the Supreme Court of Canada in the 2012 decision of R. v Ipeelee, in which the Court stated: "When sentencing an aboriginal offender, courts must take judicial notice of such matters as the history of colonialism, displacement, and residential schools and how that history continues to translate into lower educational attainment, lower incomes, higher unemployment, higher rates of substance abuse and suicide, and, of course, higher levels of incarceration for aboriginal peoples." 2 Ipeelee identifies the necessity of connecting past injustice to present-day realities for all offenders, with a particular emphasis on the unique experiences of indigenous people.This book examines how such matters of historical injustice and contemporary forms of marginalization are taken into account in criminal sentencing in Canada.I examine several legal cases, parliamentary debates, government reports, media commentary, and community sources in order to show how the interaction between narratives about past injustices and contemporary discrimination are given new meaning in Canada's sentencing regime.To highlight my specific interest in exploring how a concern with the past is used in sentencing,
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.260 | 0.111 |
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".