Bibliographic record
Abstract
Abstract Why has intersectionality fallen by the wayside of discrimination law? Thirty years after Kimberlé Crenshaw coined the term ‘intersectionality’, discrimination lawyers continue to be plagued by this question across a range of jurisdictions, including the US, UK, South Africa, India, Canada, as well as the UN treaty body jurisprudence and the jurisprudence of the EU and the ECHR. Claimants continue to struggle to establish intersectional claims based on more than one ground of discrimination. This book renews the bid for realizing intersectionality in comparative discrimination law. It presents a juridical account of intersectional discrimination as a category of discrimination inspired by intersectionality theory, and distinct from other categories of thinking about discrimination including strict, substantial, capacious, and contextual forms of single-axis discrimination, multiple discrimination, additive discrimination as in combination or compound discrimination, and embedded discrimination. Intersectional discrimination, defined in these theoretical and categorial terms, then needs to be translated into doctrine, recalibrating each of the central concepts and tools of discrimination law to respond to it—including the text of non-discrimination guarantees, the idea of grounds, the test for analogous grounds, the distinction between direct and indirect discrimination, the substantive meaning of discrimination, the use of comparators, the justification analysis and standard of review, the burden of proof between parties, and the range of remedies available. With this, the book presents a granular account of intersectional discrimination in theoretical, conceptual, and doctrinal terms, and aims to transform discrimination law in the process of realizing intersectionality within its discourse.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.034 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".