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Record W2512887929 · doi:10.5131/ajcl.2015.0007

Judging Stereotypes: What the European Court of Human Rights Can Borrow from American and Canadian Equal Protection Law

2015· article· en· W2512887929 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueThe American Journal of Comparative Law · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicLaw, Rights, and Freedoms
Canadian institutionsnot available
Fundersnot available
KeywordsSupreme courtHarmLawStereotype (UML)Human rightsRaising (metalworking)Transformative learningSociologyPolitical scienceCommon lawPsychologySocial psychology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.531
Threshold uncertainty score0.973

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.029
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.063
GPT teacher head0.313
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it