A Crack in Everything: Restorative Possibilities of Plea-Based Sentencing Courts
Bibliographic record
Abstract
Restorative justice, as a philosophy and set of practices, has traditionally been conceived of as existing separate from, indeed in opposition to, the more retributive ethic of mainstream, court-based justice proceses. Considered as such a polarized alternative, restorative justice has largely been unable to dislodge the dominant hold that formal, professionally managed public courts maintain over the resolution of criminal wrongs. Other commentators, however, argue that restorative and retributive concepts of justice are not necessarily mutually exclusive. This article explores court-based sentencing processes through a restorative lens, and suggests that while Canadian law formally privileges a retributive approach to sentencing, it also endorses practices that are more resonant with restorative values. In practice. sentencing courts that draw energy and guidance from restorative justice principles are more successful at including offenders in dialogues and determinations of just outcomes. Thus, a formally retributive sentencing framework actually benefits from the incorporation of restorative principles and practices. The marriage of these concepts of justice, is however, hampered by the antagonistic concerns of efficiency and uniformity 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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.011 | 0.024 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".