Punching above its Weight: Cuba's use of sport for South–South co-operation
Bibliographic record
Abstract
While known for training world-class athletes to compete in prestigious international competitions, Cuba is also educating 983 coaches from vulnerable communities in 53 countries at its Escuela Internacional de Educación Física y Deporte (eiefd). These athletes are bound not necessarily for the Olympic podium, but for marginalised communities where they are expected to develop sport and recreation programmes. While Cuba has garnered hard currency by training athletes from other countries, the eiefd is funded entirely by the state under the auspices of South–South co-operation. Why would Cuba, a resource-poor country, commit to training foreign coaches? This paper argues that Cuba's sport internationalism is grounded in complex and historical notions of co-operation with other countries in the global South. Through a critical analysis of state policy, and the goals of current initiatives like the eiefd, it argues that, while nationalism and foreign remuneration are a factor, the commitment to sport and development may be tied to broader goals of counter-hegemonic development. For scholars interested in Sport for Development and Peace Cuba's use of sport is noteworthy as it is not necessarily a means to development as much as a result of international social development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".