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Record W2548988095

The Standard of Proof in Phase I Merger Proceedings: The Lesson from the Microsoft/Skype Appeal

2014· article· en· W2548988095 on OpenAlexaboutno aff
Andriani Kalintiri

Bibliographic record

VenueLondon School of Economics and Political Science Theses Online (London School of Economics and Political Science) · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMerger and Competition Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsAppealCommissionContext (archaeology)Merger controlPolitical scienceRelevance (law)LawLaw and economicsLegitimacyAuthorizationEuropean commissionCompetition (biology)BusinessEuropean unionSociologyComputer scienceComputer securityPoliticsInternational trade
DOInot available

Abstract

fetched live from OpenAlex

How "certain" must the Commission be that a notified concentration will or will not impede effective competition on the market, if allowed to proceed, before it lawfully adopts a decision prohibiting or authorising it respectively? This question largely synopsises the heart of the heated discussions that the famous trilogy of merger annulments in Schneider Electric, Airtours and Tetra Laval incited. Amidst a general feeling that the evidence expectations of the European Courts had sharply increased, attempts were made to positively identify the standard of proof governing merger analysis.1 The issue is not one to take lightly. In view of the prognostic nature of merger control, what standard of proof the Commission has to satisfy determines not only the practical perception of the "significant impediment to effective competition" test as established in the EU Merger Regulation, but also the legitimacy of its decision-making. In this context, this article discusses the significance of the latest judicial insight into the problem of the standard of proof governing Phase I merger decisions as provided by the General Court in Cisco ’s appeal against the Commission’s authorisation of the Microsoft/Skype concentration.

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)0.006
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.165
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.006
Scholarly communication0.0000.001
Open science0.0020.001
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 head0.025
GPT teacher head0.280
Teacher spread0.255 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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