A NATIONAL RESOURCE CONFIGURATION LEADING TO A COMPETITIVE ADVANTAGE IN ATHLETICS: A FOUR COUNTRY BENCHMARK STUDY
Bibliographic record
Abstract
AIM OF THE PAPER A strategic approach in elite sport goes hand in hand with the increasing investment of countries. At a sport overall level, this has lead to the homogenization of elite sport development (Houlihan, 2009). At a sport specific level, Andersen and Ronglan (2011) and Newland and Kellett (2012) highlighted a growing divergence among the organization of elite sport policies. Therefore, this article seeks to explain how countries develop a competitive strategy and which policy programs exactly these countries develop to achieve such a competitive position in one specific sport, athletics. The aim of this paper is to evaluate and compare four countries’ national resource-configuration to obtain a competitive advantage in athletics (Belgium [Flanders & Wallonia], Canada, Finland & the Netherlands). Such a configurational analysis starts by the development of thematic composite indicators in order to make an evaluation of countries’ competitive position.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".