Bibliographic record
Abstract
Antitrust has grown explosively in the last quarter century. As of the end of 2004, 102 countries - from Albania to Zimbabwe - had national competition laws on their books. Together, these countries account for more than 85 percent of the world’s population. Many of these countries, including China and India, have been strengthening those laws and their enforcement. More than three fifths of the countries with antitrust laws today did not have any laws on the books before 1990, and many of those that did had ineffectual ones. Antitrust spread rapidly as country after country started relying more on markets, rather than central planning and government enterprise, to spur economic growth. Countries that embraced markets soon adopted the same sort of rules for regulating the game of competition that the United States had put in place in 1890 to rein in the excesses of laissez faire capitalism. This chapter provides a brief overview of the global antitrust enterprise as it stands at the beginning of 2009. The remainder of the volume takes deep dives into diverse jurisdictions.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.210 | 0.087 |
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".