Bibliographic record
Abstract
Racial profiling is one of the most enduring problems in policing. Yet it remains largely under-theorized, which generates important theoretical and practical implications. Racial profiling tends to be construed as an arbitrary detention rather than a form of unconstitutional discrimination. For this reason, the section 15 Charter right to equality plays little to no role in most leading cases on racial profiling. The legal framework that governs racial profiling lacks clarity and can be applied inconsistently. And the remedial landscape associated with racial profiling claims has evolved minimally. This article advances a novel approach to racial profiling that addresses these shortfalls. It demonstrates why racial profiling is wrongful primarily because it embodies discrimination that violates the section 15 Charter right to equality, and secondarily, infringes liberty or privacy interests, and in so doing, breaches other constitutional rights. It offers a simplified legal framework for how courts can better approach racial profiling in constitutional criminal procedure. Drawing on the republican theory of freedom (or republicanism), it shows why racial profiling results in domination—meaning vulnerability to unchecked threats of interference— that courts fail to control. In doing so, it deepens our theoretical understanding of racial profiling and its connection to equality and liberty. The concluding parts of this article contend that courts can incorporate two innovative remedies that can better prevent and address racial profiling: structural injunctions and constitutional settlement agreements. Ultimately, this article offers a new path forward for how racial profiling can be approached in a manner that better safeguards individuals’ fundamental rights and interests.
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 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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.032 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".