REGIONAL COMPETITION LAW AGREEMENTS: AN IMPORTANT STEP FOR ANTITRUST ENFORCEMENT
Bibliographic record
Abstract
This essay argues that regional competition law agreements on joint enforcement and advocacy (rjcas) hold an important potential to solve many of the enforcement problems that small and developing jurisdictions face and can provide additional benefits that go beyond such solutions. It also argues that the costs involved in such agreements are not prohibitive and that many of these costs can be overcome by structuring appropriate solutions. Accordingly, rjcas have the potential to create Pareto superior solutions to enforcement problems relative to unilateral enforcement. The essay then broadens the analysis to the potential effects of rjcas on non-member states. It is argued that such agreements create much lower negative externalities for non-member states and for international coordination efforts than regional trade agreements. On the contrary, they often create positive externalities for non-member jurisdictions. Accordingly, they offer important potential for strengthening competition law enforcement and should generally be encouraged. In addition, as the article shows, rjcas can further international efforts for coordination and cooperation in competition law.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".