Rules That Govern Rules: Evidence, Proof and Judicial Control in Competition Cases
Bibliographic record
Abstract
This is the introductory chapter to a 32-chapter book (Ehlermann and Marquis, eds., 2011) concerning the evaluation of evidence in competition cases, the decisions based thereon, and the manner in which courts review such assessments and decisions in terms of, for example, scope and intensity of control. A wide range of related issues is discussed, including standards of proof and standards of review as well as the treatment of economic evidence in infringement and merger cases. Reference is made to several legal regimes, such as, above all, that of the European Union and, among others, France, Germany and the United Kingdom, and the United States and Canada. The author discusses the various chapters of the book in detail and attempts to link or contrast the authors' contributions where appropriate.
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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 | 0.034 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.030 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".