The Design of Competition Law Institutions: Global Norms, Local Choices
Bibliographic record
Abstract
Competition (or antitrust) law is national law. More than 120 jurisdictions have adopted their own competition law. Is there a need for convergence of the competition law systems of the world? Much effort has been devoted to nudging substantive law convergence in the absence of an international law of competition. But it is widely acknowledged that institutions play as great a role as substantive principles in the harmonious—or dissonant—application of the law. This book provides an in-depth study of the institutions of antitrust. It does so through a particular inquiry: Do the competition systems of the world embrace substantially the same process norms? Are global norms embedded in the institutional arrangements, however disparate? Delving deeply into their jurisdictions, the chapters illuminate the inner workings of the systems and expose the process norms embedded within. Case studies feature Australia/New Zealand, Canada, Chile, China, Japan, South Africa, the USA, and the European Union, as well as the four leading international institutions involved in competition: the World Trade Organization, the Organization for Economic Cooperation and Development, the United Nations Conference on Trade and Development, and the International Competition Network; and the introductory and synthesizing chapter draws also from the new institutional arrangements of Brazil and India. The book reveals that there are indeed common process norms across the very different systems; thus, this study is a counterpart to studies on convergence of substantive rules. The synthesizing chapter observes an emerging “sympathy of systems” in which global process norms, along with substantive norms, play a critical role.
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 | 0.016 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.076 |
| Scholarly communication | 0.021 | 0.021 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".