Expressive Harms and the Strands of Charter Equality: Drawing out Parallel Coherent Approaches to Discrimination
Bibliographic record
Abstract
"Expressive harms " are rights violations that may arise from governmental expression through laws or state action, even absent material or otherwise tangible harms. Same-sex marriage provides an example: having won rights to most marriage- related economic benefits in M. v. H., gays and lesbians nevertheless fought for state recognition of their marriages in Halpern v. Canada The author delineates three conceptions of expressive harms. Among these are what may be termed "direct dignity harms"; on this conception, some forms of state expression exert effects upon human dignity without intermediate steps (for example, stereotyping) or ultimate material consequences (for example, exclusion from benefits). The author provides, in particular, an account of direct-dignity expressive harms and relates this account to the equality jurisprudence of s. 15 of the Charter. Finally, the author shows how the Supreme Court of Canada has implicitly incorporated expressive insights within s. 15, but suggests that the Court has done so with some incoherence. By failing to make explicit its reliance on several expressive and other rationales, the Court has produced an equality test with requirements derived from various conflated equality approaches, rendering the test unnecessarily onerous for some claimants.
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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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.012 | 0.115 |
| Scholarly communication | 0.024 | 0.015 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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".