Positioning in Olympic Winter sports: analysing national prioritisation of funding and success in eight nations
Bibliographic record
Abstract
Research question: Despite the attention the Olympic Winter Games has received by scholars, there has been little theoretically informed analysis on the positioning of nations in a dynamic environment. The purpose of this paper is to analyse how nations position themselves in the Winter Games by comparing national funding prioritisations of Olympic Winter sports.Research methods: The distribution of funding in 2010/2011 is used as a proxy to examine how eight nations prioritise among seven sports. National policies are analysed at two levels: (a) the concentration of funding among the supported sports is measured using the Hirschman-Herfindahl Index (HHI) and (b) the Spearman’s rho coefficient is used to examine the correlations between the distribution of funding (2010/2011) and success per sport in the past (1992–2006), recent past (2010) and future (2014).Results and findings: All nations show some prioritisation, but the resulting distribution of funding differs. For example, South Korea diversifies its funding most equally (HHI = 0.18), while Switzerland’s funding is more concentrated (HHI = 0.46). Furthermore, positioning differs depending on the type of sport most prioritised, be it skiing (Australia, Canada, Finland and Switzerland), skating (Japan and the Netherlands), both (South Korea) or bobsleigh/skeleton (Great Britain). Meanwhile, high correlation values were found for Australia, Great Britain, Finland and Japan in all periods, while the Netherlands, Canada, South Korea and Switzerland show high values in specific periods only. The results provide empirical evidence on different positioning strategies regarding the investment in either a focused or a diversified portfolio of targeted sports.Implications: Using a management perspective derived from economics, this study supports national decision-makers to compare prioritisation policies in their own national context.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".