European competition law annual 2010 : merger control in European and global perspective
Bibliographic record
Abstract
Every year, top-level market regulators, academics and legal and economic practitioners contribute to the Annual Competition Workshop organised at the European University Institute in Florence. The Co-Directors of the Workshop are Philip Lowe, Mel Marquis and Giorgio Monti. Workshop participants address and critically analyse a particular set of topical issues in the field of competition law and policy. The proceedings are published in Hart's European Competition Law Annual series. This is the fifteenth in the ECLA series. It encompasses numerous chapters that examine the field of merger control from a variety of perspectives. In these chapters the contributors discuss legal and economic issues of substantive analysis, procedure, comity and best practices, as well as matters relating to the litigation of merger cases, particularly before the European Courts. The discussion also benefited from the perspectives of policy makers and experts from Canada, China, Japan, Korea, the United States and other jurisdictions and regions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".