Understanding the acquisition and maintenance of sporting expertise: Current perspectives
Bibliographic record
Abstract
The acquisition and maintenance of high levels of sporting skill continues to fascinate large numbers of the Canadian population. In recognition of Canada's role as host of the 2015 Pan-Am and Para-Pan-Am Games, this symposium focuses on the multidisciplinary processes involved with the acquisition and maintenance of sport skill in able-bodied and para-sport athletes. The first two presentations in this symposium explore psychological factors that appear to underpin elite athletes' ability to perform deliberate practice, a variable highly related to attainment. Young et al propose a scale for measuring athletes' ability to practice, while Tedesqui et al. examine the concept of 'Consideration of Future Consequences' as a possible buffer of individuals' capability to self-regulate thereby affecting practice behaviours. From the psychological, the symposium shifts to the influence of geographic factors on athlete development. LaForge-MacKenzie et al. consider how geographic factors might constrain the development of Canadian Olympians. In the fourth presentation, Lemez et al. focus on an area of high performance sport that has received very little research attention. More specifically, they examine the developmental histories of high performance athletes in wheelchair basketball. Finally, Schorer et al. considers the maintenance of skilled perceptual performance in elite volleyball players. Collectively, these five presentations highlight the range and complexity of issues currently being considered in sport expertise research.
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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 | 0.022 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.033 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".