Class Warfare: Group Litigation of Systemic Discrimination Before Human Rights Tribunals in Canada
Bibliographic record
Abstract
Human rights tribunals in Canada have been an effective tool to address allegations of individual, or first-generation, discrimination. Unfortunately, they have generally been less effective at addressing systemic patterns of discrimination, otherwise known as second-generation discrimination. This form of discrimination remains difficult to identify and even harder to litigate. This article explores group litigation mechanisms as a means of facilitating the litigation of systemic discrimination allegations. By constructing a theoretical framework based on relevant literature, this article first identifies how the use of group litigation techniques might assist in overcoming the barriers to systemic discrimination litigation arising at each phase of the litigation process. I hypothesize that, where group litigation is available to address systemic discrimination, more claims will be filed and there will be a higher rate of positive outcomes in these cases. This theory is tested with a qualitative and quantitative survey of systemic discrimination litigation before the Human Rights Tribunal of Ontario, a tribunal without any standard group litigation procedure, and the British Columbia Human Rights Tribunal, a tribunal that has adopted the representative complaint procedure, which allows for group litigation akin to class actions. This exploratory study suggests that having a standard group litigation procedure may reduce barriers to entry for systemic discrimination claims and may improve outcomes in these cases. As such, it is recommended that legislatures amend their human rights statutes to create mechanisms allowing for the aggregation of discrimination claims.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.031 | 0.008 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".