Gaming the world: how sports are reshaping global politics and culture
Bibliographic record
Abstract
Professional sports today have truly become a global force, a common language that anyone, regardless of their nationality, can understand. Yet sports also remain distinctly local, with regional teams and the fiercely loyal local fans that follow them. This book examines the twenty-first-century phenomenon of global sports, in which professional teams and their players have become agents of globalization while at the same time fostering deep-seated and antagonistic local allegiances and spawning new forms of cultural conflict and prejudice. Andrei Markovits and Lars Rensmann take readers into the exciting global sports scene, showing how soccer, football, baseball, basketball, and hockey have given rise to a collective identity among millions of predominantly male fans in the United States, Europe, and around the rest of the world. They trace how these global — and globalizing — sports emerged from local pastimes in America, Britain, and Canada over the course of the twentieth century, and how regionalism continues to exert its divisive influence in new and potentially explosive ways. Markovits and Rensmann explore the complex interplay between the global and the local in sports today, demonstrating how sports have opened new avenues for dialogue and shared interest internationally even as they reinforce old antagonisms and create new ones.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".