Multiple Discrimination in EU Anti-Discrimination Law : Towards Redressing Complex Inequality?
Bibliographic record
Abstract
In the past years, discussions about equality law in the EU have witnessed the emergence of growing concerns about ‘intersectionality’. In cases of multiple and intersectional discrimination, victims experience differential treatment or disadvantage based on several grounds, for instance gender and race. This type of complex and multi-layered discrimination poses specific challenges to EU anti-discrimination law, which systematically tends to reduce discrimination to one single protected category. Consequently, multiple and intersectional discrimination often falls into the cracks of equality protection, raising the question of whether EU anti-discrimination law is an adequate instrument to combat intersectional discrimination. Despite rising awareness about the necessity to address this issue, neither EU legislation nor jurisprudence has provided an adequate answer so far. Rather, the warning against ‘multiple discrimination’ contained in the preambles of the Race Equality Directive 2000/43/EC (14) and the Framework Directive 2000/78/EC (3) falls short of bringing conceptual clarity. However, despite the Court’s apparent lack of understanding of the issue of intersectionality—culminating in Parris in 2016 – this chapter argues that a careful reading of the few cases of discrimination invoking multiple grounds brought to the CJEU reveals potential paths towards recognizing intersectional discrimination. This chapter reviews these pathways to recognition and demonstrates how they could contribute to a better protection of equality for victims of multiple and intersectional 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".