Le renouveau du traitement des entreprises communes en droit européen de la concurrence /
Bibliographic record
Abstract
The assessment of joint venture appears to be one of the most difficult issues in European competition law. A rational and well-balanced treatment is long awaited. Indeed, the artificial distinction between co-operative and concentrative joint ventures was based on complex and contradictory criteria. The European Commission applied them flexibly at the expense of predictability, damaging the legal certainty that is so necessary for these transactions. The existence of two distinct regimes, one for concentrative and one for co-operative joint ventures, with more favourable treatment for the former, explains why firms wished to subject their transactions to the Merger Regulation rather than article 81 of the Treaty. The reform of the Merger Regulation, which entered into force in 1998, modifies the boundaries between the different types of joint ventures, now focusing on structural criterion. It gives birth to a new distinction between structural and behavioural joint ventures. This simplification of the qualification phase, however, raises new questions relating to the content and the relevance of the chosen criterion. (Abstract shortened by UMI.)
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 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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".