The foreign models of sport governance: the case of leading sports powers
Bibliographic record
Abstract
The object of the study is organization of governance in the field of physical culture and sports in foreign countries. The subject of the study is models of sport governance on the example of foreign countries – leaders of the Olympic sports. The author of the article examines approaches to the organization of sport governance using the example of the USA, Great Britain, Canada, France, China and Germany. Special attention is paid to the forms of interaction between public sports organizations and executive authorities in the field of physical culture and sports operating in the structure of governments of these countries. The article examines the distinguished models of sport governance in the field of sports, taking into account various approaches to the interaction of public sports organizations and state authorities. Some models involve the passive participation of representatives of public sports organizations in the process of making key decisions, while other models, on the contrary, provide active participation and interaction in developing strategic decisions in the field of physical culture and sports. The study used the method of analyzing legal documents regulating the sphere of physical culture and sports, comparative analysis, systematization and generalization of basic materials published on government websites and official websites of public sports organizations of foreign countries. As a result of the conducted research, various approaches to organization of sport governance are shown in the Great Britain, France, Canada, China, the USA and Germany, which may contribute to improving the sport governance system in the Russian Federation, in order to implement the tasks outlined by the President of the Russian Federation for the period until 2030 and in the future until 2036.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 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".