An Analysis of Countries’ Organizational Resources, Capacities, and Resource Configurations in Athletics
Bibliographic record
Abstract
Research on elite sport policy tends to focus on the policy factors that can influence success. Even though policies drive the management of organizational resources, the organizational capacity of countries in specific sports to allocate resources remains unclear. This paper identifies and evaluates the organizational capacity of five sport systems in athletics (Belgium [separated into Flanders and Wallonia], Canada, Finland, and the Netherlands). Organizational capacity was evaluated using the organizational resources and first-order capabilities framework (Truyens, De Bosscher, Heyndels, & Westerbeek, 2014). Composite indicators and a configuration analysis were used to collect and analyze data from a questionnaire and documents. The participating sport systems demonstrate diverse resource configurations, especially in relation to program centralization, athlete development, and funding prioritization. The findings have implications for high performance managers’ and policy makers’ approach to strategic management and planning for organizational resources in elite sport.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".