National Courts, Global Cartels: F. Hoffman-LaRoche Ltd. v. Empagran, S.A. (U.S. Supreme Court 2004)
Bibliographic record
Abstract
In its most recent term, the United States Supreme Court heard a case arising out of the activities of a price-fixing cartel in the vitamins market. The defendants were a number of major international pharmaceuticals companies, including F. Hoffman-LaRoche, Rhone-Poulenc, Daiichi Pharmaceutical, and BASF, that had fixed prices for bulk vitamins and vitamin pre-mixes in markets around the world. The cartel, which has been described as “probably the most economically damaging cartel ever prosecuted under U.S. antitrust law,” is estimated to have affected over $5 billion of commerce worldwide. Previous proceedings against the participants in the cartel, initiated in Australia, Canada and the European Union as well as in the United States, included administrative investigations and criminal prosecutions of individual executives. In these various proceedings, the cartel participants were found to have violated antitrust laws in the United States and elsewhere, and were subjected to heavy – indeed, record – fines in many countries. By all accounts, the countries engaged in investigating and then prosecuting the cartel participants did so in full cooperation with each other. In particular, they made use of the mutual assistance and information sharing agreements that have become an important component of coordinated international antitrust enforcement.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.013 | 0.006 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".