Leveling and Spotlighting: How the European Court of Justice Favors the Weak to Promote its Legitimacy
Bibliographic record
Abstract
As private actors turn to international courts (ICs), we argue that judges can seize individual rights litigation to promote themselves as protectors of the weak. By leveling the odds for less resourceful individuals and spotlighting their rights claims, ICs can cultivate support networks in civil society. We verify this legitimation strategy by scrutinizing the first IC with private access: the European Court of Justice (ECJ). Often cast as a stealthy pro-business court, we show that ECJ judges instead publicized themselves as individual rights promoters: do they match words with deeds? Leveraging an original dataset, we find that the ECJ “levels,” favoring individuals’ rights claims compared to claims by businesses boasting larger, more experienced legal teams. The ECJ also “spotlights” support for individuals through press releases that lawyers amplify in law journals. Our findings challenge the view that ICs build legitimacy by stealth and the “haves” come out ahead in litigation.
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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 | 0.008 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".