Bibliographic record
Abstract
A major issue for US antitrust enforcement in the last year or so has been how to achieve maximum detection and deterrence of cartels, even at the cost of weakening certain sanctions. Thus, new legislation protects first-to-confess price fixers from criminal penalties and from trebling of damages owed to customers. To the same end, US enforcement agencies have sought to cut back the ability of foreign victims of the non-US aspects of worldwide cartels to obtain damage relief in American courts. This approach has been justified primarily as facilitating the operation of leniency policies by decreasing the scope, or uncertainty, of the private damage action consequences of confession. Closing US courts to foreign victims has also been justified in terms of the expressed wishes of the US allies (e.g. Germany, Japan, Canada) to fashion their own private remedy policies for their residents. In merger enforcement, trends are steady, but many litigated merger cases were decided against the Government, which could not always support its theories of probable consumer injury with hard facts. Cases involving misuse of intellectual property continue to be aggressively fought, particularly where dubious means are used to enshrine a patented invention as part of an industry standard. US efforts toward international cooperation and harmonisation have had a steady pattern of achievement, but some difficult issues of policy and practice seem intractable, particularly centralisation of merger control and harmonisation of approaches to private remedies.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".