Global Sports Expansion: Baseball, Basketball, NASCAR, Football
Bibliographic record
Abstract
In the past, athletes who represented a sports organization were comprised of local members of a community, and sports were deeply rooted in a place. Today, sporting affiliations feature athletes from around the world with little or no connection to a place as sports now transcend boundaries. The current era of globalization is characterized by increasing communications, more efficient technologies, global production, political integration, and an exchange of cultural ideals, each contributing to the compression of time and space. Major League Baseball (MLB), the National Basketball Association (NBA), the National Association for Stock Car Auto Racing (NASCAR), and the National Football League (NFL) succeeded in establishing large fan bases, retail enterprises, and media rights domestically in the United States, and to an extent in Canada. Moreover, these professional sporting organizations have taken the lead in contemporary global expansions of their respective sports in attempts to expand media coverage. This expanded media coverage will surely lead to supplemental consumerism. Basketball leads this selection of sports in terms of global expansion. The sport's minimal equipment requirements and easily adaptable rules allow the sport to be played in numerous settings. Baseball is also attempting to expand globally; however, stipulations pertaining to international competitions are limiting, making this a difficult process. Out of these four sports, NASCAR and football face the most challenges in expansion, although both seek new markets. NASCAR and football have strong roots in the United States (and Canada) and face difficulties overcoming competing sports of similar interests with much larger global fan bases, such as Formula 1 racing, soccer, or rugby.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.090 | 0.013 |
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".