A Wrench in the Social Justice Toolbox: Assessing the Constitutional Class Action as a Tool for Addressing Racial Discrimination
Bibliographic record
Abstract
ABSTRACT: Racial profiling is a form of racial discrimination exemplified by police when they target individuals because of their race or ethnicity, as opposed to their engagement in criminal behaviour. Canadian courts have recognized that racial profiling constitutes a breach of Charter rights. This is important because bringing a claim for Charter damages is one of the few options available to victims of racial profiling for pursuing compensation. Unfortunately, this remedy is seldom pursued due to financial, social, and psychological barriers. This paper discusses the utility of the class proceeding as a tool for increasing the accessibility of Charter damages for victims of racial profiling. Part A provides an introduction to racial profiling. Part B highlights the consequences of racial profiling and its recognition by Canadian courts. Part C discusses the availability and importance of Charter damages and the complementary nature of the legal frameworks for racial profiling and Charter damages. Part D canvasses the two leading Canadian cases on the certification of Charter damages claims and discusses the uncertainty that persists in the current legal landscape. Part E advocates for the use of class actions to address the inaccessibility of Charter damages and reviews the recent certification of a racial profiling claim in Quebec to support the viability of such claims. Finally, Part F concludes that certification of Charter damages class actions is possible and that further clarification of the legal framework should be pursued to increase access to justice for victims of racial profiling.
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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 | 0.054 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.009 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".