Predatory Pricing Law in Canada, Australia and New Zealand: Recent Developments
Bibliographic record
Abstract
Should the European Commission decide to review its own policy in the area of predatory pricing, the issue will prove particularly thorny: (a) predatory price-cutting is difficult to distinguish from legitimate price competition; (b) the price-cost test requires an extremely detailed investigation of the defendant's cost structure and revenues; (c) under-enforcement may cause large companies to try to establish a reputation for toughness in order to deter entry into their markets; (d) over-enforcement may encourage weak competitors to sue their more efficient, price-cutting rivals, which would, in turn, discourage them from cutting prices in the first place; (e) the issue of possible defences to predatory pricing claims is not yet settled; and (f) both the Commission and the EC courts are under pressure to adopt the recoupment requirement, which is in use in several other jurisdictions. In this context, it may be useful to look at recent developments in non-EU, non-US jurisdictions - such as the Boral judgment (February 2003) and the Qantas preliminary judgment (February 2003) in Australia, the Air Canada judgment (July 2003) and the Culhane judgment (April 2004) in Canada, and the recent judgment of the Privy Council in Carter Holt Harvey (July 2004), a New Zealand case.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".