COMING FULL CIRCLE: REDEFINING "EFFECTIVENESS" FOR ABORIGINAL JUSTICE
Bibliographic record
Abstract
Aboriginal peoples are over-represented in many adverse demographics. Most striking is their presence in the justice system. Aboriginal offenders experience the highest levels of incarceration, and later recidivism. Sentencing circles are an indigenized alternate approach to sentencing that aim to improve their justice experience. Most studies conducted on the efficacy of circle sentencing have focused on its capacity to reduce crime. The findings of such research conclude that circle sentencing is ineffective at achieving such outcomes. I propose that these are the wrong outcomes to analyze and in turn seek to research new evaluative criteria for assessing circle sentencing’s effectiveness, by focusing on its restorative capacity instead of its reductive ability alone. The legitimacy of these measures is examined by interviewing individuals from different levels of restoration and comparing findings to existing scholarship. Semi-structured interviews are used to investigate the efficacy of Mi’kmaq circle sentencing in Millbrook, Nova Scotia.
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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 | 0.017 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.012 | 0.020 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".