Predatory Pricing in Canada, the United States and Europe: Crouching Tiger or Hidden Dragon
Bibliographic record
Abstract
In the area of competition law, comparative legal scholarship can provide insights into differences in the laws of nations and can identify gaps that affect the multinational businesses subject to such laws. It also provides opportunities for convergence initiatives, such as those envisioned by the International Competition Network, to work towards filling those gaps. In this article we attempt to contribute to the literature relating to the comparative study of antitrust law through our review and analysis of the predatory pricing laws in Canada, the United States and Europe. We begin with an explanation of the economic theories behind predatory pricing. While the laws of nations are generally limited by borders: the norms of economics are not. Thus, the discipline provides a normative barometer for the analysis of such laws. The topic is timely because Canada has proposed changes to its enforcement policies in the area. Our observation is that predatory pricing has historically been an area of divergence between Canada, the United States and Europe. Our conclusion is that in a time of increased convergence in the area of antitrust, predatory pricing remains an area of divergence, and one that may see further divergence if Canada does in fact change its enforcement policy as it has suggested.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".