Grounds of Discrimination: Towards an Inclusive and Contextual Approach
Bibliographic record
Abstract
This article explores recent developments in the judicial interpretation of the grounds of discrimination in human rights law. The author maintains that courts have demonstrated a willingness to accord a large and liberal interpretation to the enumerated grounds of discrimination, drawing on examples involving discrimination on the basis of sex and disability. Nevertheless, courts have not always been willing to interpret the categories of human rights law expansively. Recently, it has been necessary to turn to the Canadian Charter of Rights and Freedoms, with its analogous grounds protection, to extend the scope of prohibited grounds in human rights legislation.A further dimension of the legal interpretation of the grounds of discrimination concerns the tension between the symmetrical and neutral language of the grounds of discrimination and the asymmetrical and unequal experience of the realities of discrimination between the groups targeted by the specific grounds.Legal protections against discrimination on the basis of sex or race, for example, do not convey the historical reality of inequality faced by women and people of colour. One response to this tension can be found in recent judicial efforts to contextualize antidiscrimination law as an integral part of our evolving understanding of substantive equality. Finally, the article explores the complexities of inequality experienced by individuals who are members of more than one group that has been historically disadvantaged and considers the extent to which a grounds-based categorical approach is attentive to the realities of multiple discrimination.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.011 | 0.083 |
| Scholarly communication | 0.028 | 0.030 |
| Open science | 0.006 | 0.021 |
| Research integrity | 0.011 | 0.019 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".