The Standard of Proof in Phase I Merger Proceedings: The Lesson from the Microsoft/Skype Appeal
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.084 | 0.245 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.048 |
| Scholarly communication | 0.026 | 0.025 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.030 | 0.033 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".