The Paradox Of Visibility: Anti-Blackness, Pathologization, And The Limitations Of Canadian Criminal Law
Bibliographic record
Abstract
This dissertation offers a quadripartite analysis of the endemic nature of anti-Black racism within Canadian criminal law, contributing to the growing body of Canadian critical race theory (CRT) literature and advancing the field of race-conscious doctrinal work on Canadian criminal law. Each chapter engages the system along different axes: (a) the temporal, procedural axis – moving from policing to the trial process to criminal sentencing; (b) the micro to the macro axis, with chapters that range from an individual case study to an analysis of a small constellation of recent court decisions, to a more theoretical analysis of foundational theories of criminal punishment and desert; (c) an axis that considers concrete and practice-related concerns, to more theoretical, with chapters ranging from practice-oriented doctrinal and policy interventions to sustained analyses of theoretical frameworks and scholarly methodology. The chapters will demonstrate how CRT analyses can explain some of the deep pathologies within the criminal legal system, demonstrate its inadequacy, and expose the paradoxicality of tackling anti-Black racism in the system by making Blackness more or less visible. While each chapter will engage different issues, they will be directed toward the overarching question of how to navigate what I have termed as the “paradox of visibility”: that is, to some extent, a focus on Blackness is disadvantageous, though a refusal to focus on Blackness can support the white supremacist myth of colour blindness and racial neutrality in the criminal legal system. Concretely, the paradox demonstrates that at the same time and in the same context, both advantages and disadvantages are associated with focusing on Blackness and with efforts to achieve race neutrality. Further, my analysis of the paradox of visibility recognizes that for Black people, 'denying difference' is not an option: Black people are read as Black and subjected to so much explicit and implicit bias that claims about 'colour-blindness' are often disingenuous or naïve. So, in many respects, and as expressed by the theory of racial realism, there is no ‘outrunning’ one’s Blackness as racism is woven into the fabric of our institutions and frames the terms of reference for societal racial ordering.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".