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Record W1533396565

The Economic Consequences and Constitutionality of Administrative Monetary Penalties for Abuse of Dominance

2013· article· en· W1533396565 on OpenAlexaffabout
Grant Bishop

Bibliographic record

VenueSSRN Electronic Journal · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLegal and Constitutional Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsConstitutionalityDominance (genetics)Position (finance)RevenueSupreme courtOrder (exchange)EconomicsCompetition (biology)Law and economicsLawBusinessPublic economicsPolitical scienceFinance
DOInot available

Abstract

fetched live from OpenAlex

The 2009 amendments to the Competition Act introduced administrative monetary penalties (AMPs) for a finding of abuse of dominant position of up to $10 million for the first order, and a $15 million for each subsequent order. The quantum of such an AMP is to be determined according to a list of or mitigating factors,” including gross revenue and profits affected by the practice, the party's financial position, the history of compliance with the Act and any other relevant factor. The author argues that this provision is both inefficient and potentially unconstitutional (following from the Supreme Court of Canada's holding in Wigglesworth), because, 1) the Act does not explicitly constrain the AMP quantum to a level that internalizes the economic impacts of the anti-competitive conduct; and, 2) some of the aggravating factors lack a coherent connection to the economic impact of anti-competitive conduct. The author concludes that the Commissioner should clarify the circumstances under which AMPs for abuse of dominance will be sought. AMPs should be calibrated to market impacts based on evidence of estimated deadweight loss and economic profits, in order to ensure that AMPs remain purely deterrent and do not reach a denunciatory magnitude. First published in the Canadian Competition Law Review, Volume 26(1); reproduced with permission of the Canadian Bar Association.

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)0.001
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.549

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.031
GPT teacher head0.244
Teacher spread0.213 · 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 designTheoretical or conceptual
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

Citations0
Published2013
Admission routes2
Has abstractyes

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