Indigenous Consumer Racial Profiling in Canada: A Neglected Human Rights Issue
Bibliographic record
Abstract
This paper examines the pervasive yet underrecognized phenomenon of consumer racial profiling (CRP) against Indigenous peoples in Canada. Drawing on sociolegal analysis, public health research, and empirical data, the authors demonstrate how CRP—manifested in routine acts of surveillance, exclusion, and humiliation in retail and service spaces—functions as a contemporary expression of colonialism and systemic racism. The work identifies both individual and collective harms, including racial trauma, internalized inferiority, and civic alienation, while framing CRP as a neglected but critical human rights issue. The authors argue that CRP exacerbates intergenerational trauma and undermines reconciliation efforts, calling for Indigenous-specific remedies such as healing ceremonies, cultural safety training, and systemic data collection reforms. By situating CRP within broader patterns of legal consciousness, systemic discrimination, and access to justice, this report is a much-needed foundational resource for advancing anti-racist practices in commercial settings and fulfilling Canada’s private-sector obligations under the Truth and Reconciliation Commission and the United Nations Declaration on the Rights of Indigenous Peoples (UNDRIP).
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.040 | 0.015 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.005 |
| 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".