The Need for a Worldwide Draft To Level The Playing Field and Strike Out The National Origin Discrimination in Major League Baseball
Bibliographic record
Abstract
Under current Major League Baseball rules, if an amateur player is a resident of the United States, Canada, Puerto Rico or other U.S. territory, he can sign with a major league team only after being selected by the team in the annual First-Year Player Draft (or sign with any team after not being selected in a draft in which the player is eligible). However, all international players who are residents of countries other than those listed above are not eligible for the draft and are free to negotiate and sign with any team when they reach the age of sixteen or seventeen years old, depending on the player's date of birth. When applying federal (Title VII) and state national origin discrimination laws to the baseball drafting system, it is evident that all draft- eligible U.S. baseball players (as well as residents of Canada and U.S. territories) could successfully state a claim of "reverse" national origin discrimination inherent in the draft. Furthermore, the national origin discrimination claim would not be preempted by national labor law that encourages collective bargaining in sports and other industries. There have been forty-five years of baseball entry drafts in which U.S. amateurs have been treated worse than foreign players, and there would be no need for discrimination litigation if Major League Baseball were to institute a worldwide draft with uniform drafting rules around the globe.
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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 | 0.014 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".