Objective justification and Prima Facie anti-competitive unilateral conduct : an exploration of EU Law and beyond
Bibliographic record
Abstract
The prohibition of anti-competitive unilateral conduct by firms with market power is not absolute, but allows for derogation. For the purposes of EU law, the ECJ has accepted that a so-called ‘objective justification’ plea may be invoked in the case of a prima facie abuse of dominance. Even though this is long-standing case law, many uncertainties remain as to its interpretation.\n\nThis thesis contains a detailed examination of this concept of ‘(objective) justification’, focusing in particular on its scope and the applicable legal conditions. The thesis submits that this concept is highly important, as it can steer Article 102 TFEU away from a formalistic approach and give ample weight to the prevalent context. \n\nAlthough the thesis focuses on EU law, it also seeks inspiration from the approach in other jurisdictions. A comparative study includes relevant cases from various EU Member States (France, Germany, Ireland, Luxembourg, the Netherlands, Spain and the UK) and non-EU jurisdictions (Australia, Canada, Hong Kong, Singapore, South Africa and the US). The study reveals that these jurisdictions have accepted the availability of a justification plea, and have dealt with the concept in strikingly similar ways.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.040 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".