Confronting our failures: Tensions in remedying systemic discrimination in Canada
Bibliographic record
Abstract
This article examines the complexities of providing remedies for systemic discrimination in light of evolving understandings of equality and justice. Despite constitutional and statutory protections affirming the right to live free from discrimination, there remains a significant gap between recognizing systemic discrimination and implementing effective remedies. The 2021 case of Canada (Attorney General) v. First Nations Child and Family Caring Society of Canada serves as a focal point to highlight the shortcomings of the current remedial framework, which often prioritizes corrective over transformative justice, reflecting a formal rather than substantive approach to equality. In this case, the Canadian Human Rights Tribunal found that Canada’s inequitable funding of First Nations child welfare services was discriminatory on the grounds of race and national or ethnic origin. It ordered Canada to compensate every child impacted, while simultaneously imposing systemic remedies such as reforming federal funding policies. By exploring the Tribunal’s decision to uphold both individual and systemic remedies, the paper argues for a more integrated approach that moves beyond the dichotomy of individual versus systemic discrimination. It advocates for co-designed remedies informed by the perspectives of affected communities, calling for a shift in how systemic discrimination is addressed within Canada’s legal framework.
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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.048 | 0.029 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 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".