Judging Stereotypes: What the European Court of Human Rights Can Borrow from American and Canadian Equal Protection Law
Bibliographic record
Abstract
The concept of stereotype is novel in the case law of the European Court of Human Rights. The ECtHR has started to refer to stereotypes in several recent judgments concerning, notably, race and gender equality. In contrast, anti-stereotyping has long been a central feature of both American and Canadian equal protection law. Offering a comparison of the legal reasoning of the ECtHR and the U.S. and Canadian Supreme Courts, this Article uncovers both the pitfalls and the potential of the stereotype concept to advance transformative equality. It is hard to develop a proper legal response to stereotyping, as not all stereotypes are bad and, moreover, laws are inevitably based on generalizations. At a minimum, this Article argues, courts should name stereotypes well and carefully examine their harm. This comparative analysis shows that, at its best, legal reasoning can expose and target the invidious cycle wherein stereotyping and discrimination perpetuate each other. Both the U.S. Supreme Court and its Canadian counterpart, however, show a tendency to equate stereotypes with unfair generalizations. This Article cautions against that. Stereotypes can indeed be inaccurate or negative, but they can also be statistically correct, or prescriptive. When stereotypes are conceived of too narrowly (as only raising issues of accuracy), the concept loses its ability to strengthen a transformative equality analysis. This Article first charts and critiques the emergent ECtHR case law on stereotypes. It then offers a fresh analysis of the strengths and weaknesses of the U.S. and Canadian Supreme Courts' treatment of stereotypes. Two deceptively simple questions will form the leitmotif throughout the comparison: (i) how do these courts conceive of stereotypes, and (ii) given that stereotyping is not necessarily always negative or problematic, how do these courts determine whether the application of a stereotype is invidious? It concludes by exploring what the ECtHR can borrow from American and Canadian equal protection analysis.
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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 | 0.017 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.018 | 0.056 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".