Early specialization and talent development in figure skating: Elite coaches’ perspectives
Bibliographic record
Abstract
Among the many issues explored by sport science researchers, one topic that seems the least controversial is the recommendation against specialization in early sport training. However, there has been little examination of early specialization in sports where this type of training is the norm. In this study, we explored notions of early specialization and its consequences (i.e psychological, social, and physical) among 7 figure skating coaches (5 males and 2 females) responsible for coaching singles skating (both women and men) at a range of competition levels. Semi-structured interviews with each coach were subjected to a thematic analysis. Themes identified through the analysis revealed coaches were aware and attentive to the potential risks of early specialization and took steps to manage these risks. Moreover, the coaches noted that developing a skater in the environment of elite figure skating required identifying athletes with the right combination of characteristics, and that there were important nuances to developing these athletes in the Canadian context. These findings highlight the complexities of developing a well-rounded athlete in a sport where early specialization is widely accepted as the norm.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".