The development of sport expertise: Current issues and different perspectives
Bibliographic record
Abstract
The Great British Medalist Project (see Rees et al., 2016; Hardy et al., 2017) exemplifies the efforts to understand the development of sport expertise, as well as the complexity and multidimensional nature of athlete development. In the same spirit, this symposium focuses on the multidisciplinary constraints involved in the development of sport expertise. The first three presentations in this symposium explore popular secondary factors that constraint athlete development. Schorer et al explore the influence of different types of relative age effects (age within participation cohorts) on indicators of performance in football players (i.e., monetary value), while Smith and Weir examine the influence of relative age and level of competition on dropout from female developmental soccer in Ontario over a 7 year period. Farah et al examine the influence of geographic factors related to early athlete development environments (i.e., community population density and proximity to major developmental programs) on the development of Canadian National Hockey League draftees. In the fourth presentation, Wilson and colleagues also examine early developmental environments in their study of the relationship between an athlete's skill level and their family's (parent and sibling) physical activity and sport participation patterns. In the fifth presentation, Tedesqui and Young examine practice behaviour. Specifically, they describe the longitudinal influence of psychological grit (perseverance of effort and consistency of interests) on athlete's level of practice engagement. To conclude, our discussant, Joseph Baker, will discuss the implications of these presentations for future research, theoretical models athlete development, and talent identification and development programs.
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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 | 0.023 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.006 | 0.031 |
| Scholarly communication | 0.017 | 0.035 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.015 | 0.019 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".