Bibliographic record
Abstract
Introduction Much of baseball's lore centers on the game's meaning in the United States. “Where have you gone, Joe DiMaggio? A nation turns its lonely eyes to you,” sang Simon and Garfunkel. However, the US national pastime is a global game. As the focus on Japan and East Asia elsewhere in this volume suggests, a understanding baseball today requires a global perspective, but this perspective has to be more comprehensive than simply appreciating the skills of foreign players. This chapter analyzes the globalization of baseball in Latin America, which has been the main source of foreign talent for Major League Baseball (MLB). In Latin America, the globalization of baseball has provided uplifting stories of players who emerged from Third World poverty to star in “The Show,” as the American major league game is sometimes called. But it has also involved many problems MLB has struggled to address effectively in tapping into Latin America as a source of talent. This chapter explores the evolution of MLB's efforts to govern its activities in Latin America. To begin, we detail the dominance of the Dominican Republic and Venezuela as sources of players for MLB. The prominence of Latin American players flows from MLB’s efforts to globalize the market for baseball labor. The globalization of baseball operates differently in Latin America than in Asia and other regions, and a key difference is how MLB’s recruiting efforts in Latin America predominantly target children. In response to criticism that it was operating a system that discriminated against and mistreated Latin American children, MLB has undertaken reforms, but, even as some past problems diminished in seriousness, MLB has had to confront new ones, such as abuse of performance-enhancing drugs by Latin American minor league players.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.043 | 0.005 |
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".