Talent Research in Sport 1990–2018: A Scoping Review
Bibliographic record
Abstract
Several recent systematic and targeted reviews have highlighted limitations in our understanding of talent in sport. However, a comprehensive profile of where the scientific research has focused would help identify gaps in current knowledge. Our goal in this scoping review was (a) to better understand what others have done in the field of research (e.g., what groups have been examined using what research designs and in what areas), (b) to summarize the constituent areas of research in a meaningful way, (c) to help identify gaps in the research, and (d) to encourage future research to address these gaps. Peer-reviewed articles written in English that met several inclusion criteria were analyzed. A total of 1,899 articles were identified, and the descriptive findings revealed a relatively narrow focus of research on talent in sport. Specifically, the majority of examined articles focused on (a) males only, (b) the sport of soccer, (c) perceptual cognitive variables, (d) developing athletes, (e) adult samples, and (f) cross-sectional designs. For better or worse, the concept of talent remains a central element of how coaches, practitioners, and scientists think about athlete development. Findings from this scoping review highlight the continued need to explore issues related to talent identification, selection, and development in more diverse samples (e.g., female athletes and younger ages) and contexts (e.g., from Africa, Asia, and South America). There is also a clear necessity to focus on under-researched areas using alternative methodologies.
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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 | 0.015 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.037 | 0.033 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".