Unraveling the legal standard applicable to cartels: a comparative study of European and North American antitrust
Bibliographic record
Abstract
In antitrust law, evidentiary requirements are classified as issues of substantive law or as issues of procedural law. But this dichotomy disconnects inseparable dimensions of the legal standard, a pivotal point of reference for corporations since it determines what is required to prove the existence of a cartel. This thesis aims at comparing the legal standard applicable to cartels in the United States, the European Union and Canada. It will be demonstrated how the legal standard enables courts to shape an increasingly economics-based antitrust policy. Two important values are then at stake: enforcement efficiency and legal fairness. These values, in the context of globalization, plead for improvements and a convergence of the standards: the United States must beware of the growing uncertainty in antitrust; Canada must expand the scope of per se provisions; the European Union must strive to clarify the standard of proof.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".