Effectuating Change: A Tool Box of Strategies for Reducing the Unnecessary Use of Administrative Court Orders
Bibliographic record
Abstract
This article is a sequel to Correctional Afterthought, in which the author argued that Gladue’s promise of reducing Indigenous over-incarceration by employing non-custodial measures has been thwarted. By insisting on alternatives to incarceration, the justice system is forced to rely on administrative court orders managed by provincial probation services. The judiciary and justice system participants possess a misplaced faith in the probationary regime, which functions as a repressive system of control that necessarily views the Indigenous accused as a risk that must be managed. The most common probation conditions, far from fostering reintegration, serve to erode individual autonomy, engender mistrust, alienation, resentment, and resistance; in the end creating disunity and discord. The aim of Effectuating Change is to offer a sound proposal for legislative reform and in the interim, practical sentencing solutions to deliver the true intention of Gladue and its offspring. Regardless of whether the proposals in this article are vigorously critiqued, supported, denounced or modified the hope is that they create a springboard for creative solutions to the problems identified in Correctional Afterthought.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".