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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".