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Record W1642029380 · doi:10.1177/203228441000100105

A Pathology of Business Cartels: Original Sin or the Child of Regulation?

2010· article· en· W1642029380 on OpenAlexaboutno aff
Christopher Harding

Bibliographic record

VenueNew Journal of European Criminal Law · 2010
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLaw, Economics, and Judicial Systems
Canadian institutionsnot available
Fundersnot available
KeywordsCartelSanctionsEnforcementLaw and economicsPolitical scienceImprisonmentCollusionLiabilityLawBusinessCriminologyEconomicsSociology

Abstract

fetched live from OpenAlex

Over the last 10 to 15 years there has been a significant movement towards criminalisation of involvement in business cartels in a number of jurisdictions around the world, outside of the US and Canada where such conduct has been a criminal offence since the end of the nineteenth century. In particular there is now an emphasis on individual or personal as distinct from corporate liability and on the use of severe sanctions such as imprisonment. For European jurisdictions, this represents a significant shift in enforcement strategy and the perception of activities such as price fixing, market sharing and bid rigging. Observation of these legal developments inevitably gives rise to the question: what is so bad about cartel conduct as to merit censure and sanctioning through the processes of criminal law? However, a clear consensus on the specific nature of cartel delinquency remains elusive. The American approach has been to identify an element of conspiracy in the criminality of what are now commonly referred to as hard core cartels. Elsewhere, there are ill-thought out analogies put forward with theft, fraud and dishonesty. The argument in this paper seeks a justification for criminalisation in the upward spiral of enforcement activity and consequent cartel defensiveness, turning cartelists into ‘cognisant, contumacious and covert’ actors who are evolving an increasingly determined outlaw identity in the face of an increasingly assertive global enforcement enterprise.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.935
Threshold uncertainty score0.467

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.039
GPT teacher head0.231
Teacher spread0.192 · 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

Citations5
Published2010
Admission routes1
Has abstractyes

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