'Whoa'-Ing Equine Clones’ Registration: Establishing Procompetitive Benefits to Counter the Anticompetitive Argument Against the American Quarter Horse Association's Ban on Clones.
Bibliographic record
Abstract
This Note examines Abraham and Veneklasen Joint Venture v. American Quarter Horse Association, in which a United States district court ruled that the American Quarter Horse Association’s rule banning clones of registered quarter horses from also being registered violated section 1 of the Sherman Antitrust Act. The author explores potential procompetitive justifications that AQHA has established for its rule, including the negative impact clones would likely have on the genetic variation of the breed and genetic diseases. The author argues that the district court erred by overlooking the plausibility of the justifications and that the rule of reason analysis should have been conducted. Finally, the author concludes that AQHA, like other associations that essentially create the “product” in question, must be afforded the opportunity to present procompetitive benefits and have these benefits considered by the court.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.013 | 0.009 |
| 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".