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Record W2256520808

Predatory Pricing Law in Canada, Australia and New Zealand: Recent Developments

2005· article· en· W2256520808 on OpenAlexaboutno aff
Cyril Ritter

Bibliographic record

VenueSSRN Electronic Journal · 2005
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicTaxation and Legal Issues
Canadian institutionsnot available
Fundersnot available
KeywordsPredatory pricingCommissionContext (archaeology)EnforcementReputationCompetitor analysisOrder (exchange)Competition lawCommon lawRevenueLaw and economicsCompetition (biology)BusinessEuropean commissionEconomicsLawPolitical scienceEuropean unionInternational tradeFinanceMarket economyGeographyMarketing
DOInot available

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.915
Threshold uncertainty score0.401

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.231
Teacher spread0.214 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2005
Admission routes1
Has abstractyes

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