Citizen Participation in Canadian Criminal Justice: The Emergence of 'Inclusionary Adversarial' and 'Restorative' Models
Bibliographic record
Abstract
Changes in how citizens relate to criminal justice are in the air. Perennial demands to "get tough on crime" continue. But as a society, we are "getting smart about getting tough. As the new Youth Criminal Justice Act and the relatively recent amendments to the sentencing provisions of the Criminal Code attest, Canada is at the forefront of international developments which acknowledge the importance of integrating restorative and more traditional paradigms of criminal justice. Moreover, the character of traditional criminal justice is changing. All these initiatives are rooted in varying degrees of citizen alienation from what traditionally happened in our criminal justice system. Individual citizens and identifiable communities are demanding greater participation in the administration of criminal justice. Courts, legislators and criminal justice policy makers are responding to these demands with measures, sometimes ad hoc and sometimes more coordinated, to increase the capacities of those affected by criminal harms, and their procedural aftermath, to participate meaningfully in effective societal responses to these wrongs. Meanwhile there is ferment in the scholarly literature about concepts of citizenship and theories of democracy. This paper is intended to sketch a map of these changes and reflect on the various new trails that are being blazed over what might otherwise be thought to be somewhat familiar territory.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.058 | 0.048 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.005 | 0.006 |
| 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".