The "passing on" argument in the context of private enforcement of competition law rules
Bibliographic record
Abstract
Several decades ago, the U.S. Supreme Court rendered two landmark decisions in the field of antitrust damage law---"Illinois Brick" and "Hanover Shoe"---that both dealt with the problem of "passing on" supra-competitive overcharges. Neither in Canada non in Europe, "passing on" has been an issue but until recently, since a trend has developed in both legal orders to shift the enforcement of competition laws from public to private agents. The aim of the thesis is to evaluate different policy options for the "passing on" problem. The analysis is based on a general reflection on the rationales of private enforcement and examines the economics of "passing on". It is argued that since compensation is hard to achieve, it is dubious whether private enforcement can be defended on the grounds that private litigants are more efficient enforcers than public agents.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".