Cartel Criminalization in Europe: Addressing Deterrence and Institutional Challenges
Bibliographic record
Abstract
This Article analyzes cartel criminalization in Europe from a deterrence and institutionalperspective. First, it investigates the idea of criminalizationby putting it in perspective with the more general question of what types of sanctions a jurisdictionmight adopt against collusive behavior. Second, it analyzes the institutional element of criminalization by (1) discussing the compatibility of administrative enforcement with the potential de facto criminal nature of administrative fines under European law and (2) evaluating the trade-offs between an administrative and a criminal model of enforcement. Although a panoply of sanctions against both corporations and individuals may be necessary under a deterrence perspective, this Article suggests that individualsanctions are unlikely to become a priority in Europe without a prior willingness to reform the current model of enforcement to increase the levels of due process. The debate concerning the right to a fair trial in antitrustproceedings and reforms to improve the efficiency-due process trade-off could be leveraged to open the door to the introduction of individual sanctions at the European level.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".