Equality in Canada: A Tale of Non-Normative Groups Struggling With Grounds of Discrimination
Bibliographic record
Abstract
This article intends to address the limits associated with a rigid grounds-based approach to equality, requiring claimants to categorize their identity within an enumerated ground to “deserve” the protection of the equality guarantee. To this end, I first shed light on the irreconcilability of rigid grounds with post-structuralist accounts of identity, and then lay claim to an approach to equality that extends its reach to fluid, intersectional groups. Thereafter, taking Canada as a case study, I parse out the Canadian equality jurisprudence, particularly the cases offering an analysis of the aforementioned grounds. I then move to sketch out two proposals to overcome the risks associated with the current equality jurisprudence, by focusing on marital status discrimination. I ultimately offer a cursory overview of the complex interplay between approaches to equality and the organization of interest groups, and illustrate the issues around the organization of “post-identity groups”.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.061 | 0.039 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".