“I Can’t Teach You to Be Taller”: How Canadian, Collegiate-Level Coaches Construct Talent in Sport
Bibliographic record
Abstract
Talent identification and development are two of the most critical, yet underexplored, areas in sport sciences. Despite its importance to a host of sport stakeholders, there is a void in our understanding of how coaches construct talent. In an effort to learn more, semistructured interviews were conducted with nine (one female and eight male) collegiate-level coaches from a single Canadian institution. Social constructionism was utilized as the theoretical framework to guide this research. Reflexive thematic analysis generated two main themes: “what talent looks like” and “how talent behaves.” For the former, two subthemes, physical and psychological attributes, were highlighted through the coaches’ experiences as qualities they believe talented athletes may present. The latter reflected opinions that talent may be multidimensional and context-specific in nature. Interestingly, the coaches suggested the context and circumstances of collegiate sport may nudge them to consider other elements (i.e., academic standing, years to degree completion) during talent identification that are unique to this context. Future work in this area could seek to study other populations of coaches to provide a deeper analysis of how talent is situated in relation to different sociocultural worlds.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.012 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".