How Countries Compete for Success in Elite Sport: A Systematic Review
Bibliographic record
Abstract
The ‘Global Sporting Arms Race’ is the term that describes the competition among different countries to succeed in international sports competitions. The development of that peaceful competition determines two outputs: an increase in soft power at the international level and a promotion of the national identity and social impact. It means increasing the level of influence that the countries obtain internationally as a cornerstone of the concept of a sporting nation with a proud and healthy population. In order to explain the factors involved in the success of a sports system at the elite level, a systematic review was carried out based on the PRISMA protocol in the databases Scopus, SPORTDiscus, and Web of Science. The findings of the study show that the factors that determine success at the international level have received increased attention, as shown by the number of publications since 2010. The results indicate the following research factors: (1) it was observed that most researchers tend to carry out comprehensive analyses with a holistic perspective, while the UK, Australia, Canada, and Spain carry out segmented analyses; (2) Olympic sports—especially athletics—were the most analysed; while in non-Olympic sports, those with social influence predominate in countries, such as netball; (3) the analysis of meso and micro factors is preferred over macro factors; (4) quantitative studies are preferred through the analysis of primary sources, such as official reports; and (5) the economic variable is the most common input, with medals reached at the elite level being the most used output to check the correlation or significativity of the results.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".