Making Sense of the Shift in Paradigm on Cartel Enforcement: The Case for Applying a Desert Perspective
Bibliographic record
Abstract
Soon after the coming into force of changes to the criminal provisions in the Competition Act, the commissioner of competition signalled that cartel enforcement would start to reflect a new mindset, one that treats cartels as truly criminal. But while the impetus for this shift in paradigm is well-intentioned—to give effect to a stronger criminal law mandate following the amendments—it is poorly explained, because its defenders continue to refer to the predominant deterrence rationale used in competition law, even though applying a harm-based view of crime and punishment to cartels fails to explain why criminal enforcement is needed. I believe that applying a desert perspective offers a compelling alternative explanation for this shift toward treating cartels as truly criminal. Drawing on the work of Arthur Ripstein, I offer an account of cartel enforcement that focuses on the inherently wrongful disregard for competition that characterizes cartels. I argue that seeing cartels as a particularly serious misuse of the competitive system, one that is so fundamentally at odds with the notion of a competitive marketplace that it cannot be tolerated, is what justifies recourse to the consistent and uniquely public response of the criminal law. Seen in this light, bringing a more criminal law-oriented mindset to bear on cartel enforcement makes sense in way that this shift in paradigm does not when justified in deterrence terms.
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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 | 0.022 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.052 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.014 | 0.016 |
| Insufficient payload (model declined to judge) | 0.003 | 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".