Talent identification and development in an English Premiership rugby union club: the perspectives of players and coaches
Bibliographic record
Abstract
The pathway towards senior professional status in sport is affected by a multitude of factors. An abductive examination of the talent identification and development processes at an English Premiership rugby union (RU) club was undertaken for the present study. Part one examined the perspectives on the selection and development processes of senior academy male players (n = 8), whereas part two explored the perceptions of male coaches (n = 7). A total of three focus groups were used. Three main themes were confirmed by players and coaches: (a) task constraints, (b) performer constraints, and (c) environmental constraints. Specifically, although athletes and coaches believed that performer constraints were highly impactful on players' career in RU, there were inconsistencies surrounding the task and environmental constraints. Despite an indication that three common themes impacted an players path, this preliminary study shows an imbalance in the understanding of some of the key factors perceived to be important for talent progression in the present rugby academy. More research using similar qualitative methods is recommended to better understand the differences in opinions between players and coaches. Meanwhile, practitioners should consider implementing objective and holistic strategies to improve the talent pathway in English RU academies.
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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 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".