The New History of Sport in Latin America. Introduction
Bibliographic record
Abstract
Thirty-five years ago, at a time when few were undertaking academic studies of sport and society in Latin America, the historian Joseph L. Arbena argued that sport could provide a window into the regional experience in the Americas. He also argued that the subject of sport had only received “limited systematic analysis.” To a large extent, that is no longer the case. Ruminating on that change, Gregg Bocketti recently affirmed that “now it is a rare season that does not see the publication of at least one academic work on Latin American and Caribbean sport, as scholars have joined athletes, coaches, journalists, and fans in acknowledging the immense scale and broad scope of sports in the region.” Indeed, since Arbena’s gloomy diagnosis, in most social sciences and humanities disciplines, over the past fifteen years in particular, researchers with an interest in the historical analysis of society have opened Arbena’s metaphorical window and have emphasized five key sets of issues, among many. Their approach has often been inter-disciplinary and not necessarily inclusive of history as a discipline. We identify below these five problem sets and illustrate each with an example from the growing body of literature.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".