Bibliographic record
Abstract
Following World War II, liberal nation-states sought to address injustices of the past. In keeping with trends in other countries, Canada's government began to consider its own implication in various past wrongs, and in the late twentieth century it began to implement reparative justice initiatives for historically marginalized people. Yet despite this shift, there are more Indigenous and racialized people in Canadian prisons now than at any other time in history. In To Right Historical Wrongs, Carmela Murdocca brings together the paradigm of reparative justice and the study of incarceration to examine this disconnect between the political motivations for amending historical injustices and the vastly disproportionate reality of the justice system � a troubling reality that is often ignored. Drawing on detailed examination of legal cases, parliamentary debates, government reports, media commentary, and community sources, Murdocca presents a new perspective on discussions of culture-based sentencing in an age of both mass incarceration and historical amendment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".