Judicial Depictions of Responsibility and Risk: The Erasure of State Accountability in Canadian Sentencing Judgments Involving Indigenous People
Bibliographic record
Abstract
This dissertation is set within the context of Canadas mass imprisonment of Indigenous people and centres on a critical evaluation of reported sentencing judgments. In particular, the dissertation examines some of the ways in which sentencing judges both draw attention to, and obscure, state accountability. The dissertation demonstrates that sentencing judges erase the role of the state in the criminalization of Indigenous people and in the construction of Indigenous people as risky. The result is that sentencing judgments rationalize and support the re-entrenchment, rather than the redressing, of the states oppression of Indigenous people. The dissertation is theoretical and descriptive, critically examining sentencing judges portrayals of Indigenous people and the state. The case studies are disheartening: the studies illustrate a few different ways in which sentencing law, despite purportedly aiming to repair systemic harm, continues to cement such harm. Yet the theoretical tools used to dissect sentencing judgments destructive practices can also assist in thinking through possibilities for change. The dissertation draws on theories that engage with the centrality of relationships in peoples lives (including peoples relationships with the state), the role of the state in generating and sustaining inequality, the interconnections between state efforts to contextualize Indigenous people and the reinforcement of stereotypes, and the resilience, strength, and diversity of Indigenous Peoples, communities, families, and individuals. These theories all support some existing proposals (and some current practices and possible new proposals) for pursuing decarceral approaches. The decarceral approaches that this dissertation addresses recognize that any sentencing analysis (including an analysis of how to assign responsibility for past criminalized conduct and an analysis of how to protect a community in the future) requires a consideration not only of criminalized individuals experiences but also of the states actions and inactions. A sentencing analysis must see and identify the state as having contributed to the criminalization of Indigenous people and to the construction of Indigenous people as risky. Additionally, the state must take accountability for its actions in historically and contemporarily inflicting violence on Indigenous people and for its potential to instead support Indigenous peoples resilience, safety, and sovereignty.
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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 | 0.012 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.033 | 0.023 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".