Organisational Support for High-Performance Athletes to Develop Financial Literacy and Self-Management Skills
Bibliographic record
Abstract
This paper reports the results of analysing desk-based data on organisational support for high performance athletes to develop their financial literacy and self-management skills when transitioning out of sport. There are two research questions: (1) Do sport organisations provide support schemes or other interventions such that high-performance athletes develop their financial literacy and self-management skills? and (2) Do sport organisations provide financial support schemes for high-performance athletes’ retirements? If so, what do they involve? Desk-based data collection was applied to 23 sporting organisations; these comprised 21 national organisations representing 19 countries, the International Olympic Committee (IOC) and the Oceanic National Olympic Committee (ONOC). Fifteen of the 23 organisations, representing 14 countries, provided some support or interventions on financial planning and self-management within their career assistance programmes. The findings also indicate that most organisations in 17 different countries did not provide any financial support for athletes’ retirements. While a number of sport organisations have developed appropriate interventions to assist high-performance athletes to develop financial literacy and self-management skills, such schemes appear only to be provided to high-performance athletes who have competed at the highest level e.g., Olympics, world championships, etc. Support for athletes at lower levels should also be developed and delivered by national governments, or by national sport organisations.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".