Anti-discrimination Case Law of the Court of Justice of the European Union before and after the Economic Crisis
Bibliographic record
Abstract
Abstract Income inequality is at an all-time high in the Europe Union (EU). Implications from the economic crisis which broke out in 2008, and in particularly the austerity measures introduced by Governments in Eurozone countries receiving bailout programmes, created further inequalities, for example between men and women. This paper starts from the hypothesis that whereas other institutions in the EU have played a direct role in tackling the economic crisis, the Court of Justice of the European Union (CJEU) may have played a more indirect role, which nonetheless can have an overlooked value in particular for setting direction for legal norms of equality and anti-discrimination in Europe. The paper therefore addresses a legal-empirical question: To what extent does the anti-discrimination case law of the CJEU reflect the increased inequalities in Europe following the economic crisis? Based on a dataset of all anti-discrimination cases of the CJEU, I conduct a quantitative analysis of changes in the case law from before to after the economic crisis. I find that there is only weak evidence, which suggests that the case law of the CJEU reflects the increased inequalities following the economic crisis.
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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 | 0.012 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".