Integrating public and private enforcement : implications for courts and agencies
Bibliographic record
Abstract
Perchance to dream: Well Integrated Public and Private Antitrust Enforcement in the European Union Mel Marquis Part I Designing a Balanced System: Damages, Deterrence, Leniency and Litigants' Rights I Andrew I Gavil, Designing Private Rights of Action for Competition Policy Systems: The Role of Interdependence andthe Advantages of a Sequential Approach II Tom Ottervanger, Designing a Balanced System: Damages, Deterrence, Leniency and Litigants' Rights III Scott Campbell and Tristan Feunteun, Designing a Balanced System: Damages, Deterrence, Leniency and Litigants' Rights - A Claimant's Perspective IV Donald I Baker, Trying to Use Criminal Law and Incarceration to Punish Participants and Deter Cartels Raises Some Broad Political and Social Questions in Europe V James S Venit and Andrew L Foster, Competition Compliance: Fines and Complementary Incentives Part II Integrating Public and Private Enforcement in Europe: Legal and Jurisdictional Issues I Fred Louis, Promoting Private Antitrust Enforcement: Remember Article 102 II Jochen Burrichter and Enno Ahlenstiel, Integrating Public and Private Enforcement in Europe: Legal and Jurisdictional Issues - The German Perspective III Luis Silva Morais, Integrating Public and Private Enforcement in Europe: Legal Issues IV Assimakis P Komninos, The Relationship between Public and Private Enforcement: quod Dei Deo, quod Caesaris Caesari V Barry E Hawk and Yolaine Seaton, US Antitrust Arbitration Part III Options for Collective Redress in the European Union I J Thomas Rosch, Designing a Private Remedies System for Antitrust Cases - Lessons Learned from the U.S. Experience II James Keyte, Collective Redress: Perspectives from the US Experience III Brian A Facey and David Rosner, Collective Redress for Cartel Damages in Canada IV Mario Siragusa, Options for Collective Redress in the EU V Silvia Pietrini, The Future of Collective Damages Actions in Europe Part IV Drawing Lessons and Conclusions I John Ratliff, Integrating Public and Private Enforcement of Competition Law: Implications for Courts and Agencies II Ian S Forrester and Mark D Powell, Market Forces and Private Enforcement: A Start But Some Way Still to Go III Bruno Lasserre, Integrating Public and Private Enforcement of Competition Law: Implications for Courts and Agencies IV Horst Butz, Integrating Public and Private Enforcement in Europe: Issues for Courts V Philip Lowe, Conclusions Part V Private Damages Claims and the Elusive Futur I Veljko Milutinoviae, The 'Right to Damages' in a 'System of Parallel Competences': A Fresh Look at BRT v SABAM
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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 | 0.064 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.020 | 0.057 |
| Scholarly communication | 0.065 | 0.069 |
| Open science | 0.007 | 0.022 |
| Research integrity | 0.050 | 0.024 |
| Insufficient payload (model declined to judge) | 0.018 | 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".