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Record W3096502548 · doi:10.1093/jeclap/lpaa080

Towards a Higher Standard of Proof and a More Interventionist Judicial Review in Antitrust Cases Involving Complex (Economic) Assessments Following <i>CK Telecoms</i>?

2020· article· en· W3096502548 on OpenAlexaboutno aff
Kyriakos Fountoukakos, Camille Puech-Baron

Bibliographic record

VenueJournal of European Competition Law & Practice · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMerger and Competition Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsDominance (genetics)Standard of reviewCommissionLawCLARITYCompetition (biology)Competition lawEuropean commissionLaw and economicsPolitical sciencePerspective (graphical)Judicial reviewEconomicsMonopolyComputer scienceEuropean unionInternational trade

Abstract

fetched live from OpenAlex

In the wake of the recent CK Telecoms judgment of the General Court, this article advocates for more clarity and a higher standard of proof and standard of review in antitrust, and in particular abuse of dominance, cases. Some legal concepts are like fashion: they come back in the spotlight every 20 years. This is the case for the standard of proof and standard of review which have fuelled debates among competition law fashionistas almost 20 years ago in the wake of the Tetra Laval,1 Schneider,2 and Airtours3 judgments of the then Court of First Instance of the European Union4—the first judgments to have annulled Commission merger prohibition decisions—and are now found to be trendy again, both from an antitrust and a merger control perspective. On the antitrust front, this renewed interest for the standard of proof and standard of review is due to the reflection around envisaged changes to the Commission’s enforcement powers set out in Regulation 1/20035 (so-called ‘Regulation 2’) as well as recent proposals by the Commission to gain more powers to conduct market investigations and impose remedies.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.964
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.073
GPT teacher head0.326
Teacher spread0.252 · 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.

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

Citations1
Published2020
Admission routes1
Has abstractyes

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