Is it time to retire ‘talent’ from discussions of athlete development?
Bibliographic record
Abstract
The word “talent” is used across many sport disciplines – to describe an athlete’s prowess (i.e. “he is talented”), as a term for what is sought after during assessment and selection (i.e. talent selection camps) or in reference to players to be developed (i.e. “a group of talents”). While the term has received research attention regarding its definition and criteria, its utility in practical settings is often debated. In this paper, we review several areas of concern researchers have raised for using the term “talent” and why this matters in the context of athlete development. While the notion of talent continues to resonate with coaches, scientists and practitioners, we suggest several areas for future research and recommendations for the use of this controversial term.
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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 | 0.033 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.037 |
| Scholarly communication | 0.019 | 0.024 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.007 | 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".