New Frontiers of Consumer Protection: The Interplay Between Private and Public Enforcement
Bibliographic record
Abstract
Collective redress has gained momentum in Europe and North America. Legal reforms are driven by different institutional conditions but show a limited degree of convergence. The seventeen contributions from the US, Canada and from the Member States of the European Union (East and West) put the interplay of public enforcement and private collective judicial enforcement into perspective. The parameters of analysis are the constitutional dimension (i.e. three multi-level systems compared US, EC and Canada, vertically: allocation of powers between levels and distinction between regulation and private law and administrative control versus judicial control, horizontally: degree of harmonisation, transborder litigation, choice of law), the institutional dimension (the players, regulatory and judges, private organisation and lawyers) and the substantive dimension (regulation through administrative law versus regulation through tort or contract law, standard setting (strict liability versus negligence), remedies (injunctions and damages)). These three parameters are then broken down into five sections: general comparison, the public/private enforcement divide, remedies concerning the distinction between injunction and damages as well as between class actions and group actions, negotiation and adjudication, transborder litigation and international private law. The final chapter develops prospects to indicate the way ahead.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".