Bibliographic record
Abstract
Mancur Olson claimed that concentrated interests win against diffuse interests even in advanced democracies. Multinational companies, for example, work well in unison to suit their interests. The rest of the public is not motivated or informed enough to resist them. In contrast, other scholars argued that diffuse interests may be able to fight back, but only when certain conditions prevail. One of the conditions for the success of diffuse interests is the intervention of national and international courts. Courts are able to fix problems affecting diffuse interests. Courts can also indirectly empower diffuse interests by initiating deliberation to inform the public. This paper investigates the jurisprudence of the European Court of Human Rights and the Court of Justice of the European Union. It argues that these international courts help consumers, a diffuse interest group, succeed in their struggle against internet companies, a concentrated interest group
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.015 | 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".