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Intersectional Discrimination

2019· book· en· W4238306344 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

Venuenot available
Typebook
Languageen
FieldSocial Sciences
TopicDiscrimination and Equality Law
Canadian institutionsnot available
Fundersnot available
KeywordsComputer sciencePsychology

Abstract

fetched live from OpenAlex

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.671
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.003

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.

Opus teacher head0.059
GPT teacher head0.352
Teacher spread0.294 · 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

Quick stats

Citations68
Published2019
Admission routes1
Has abstractyes

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