Examining equity, diversity, and inclusion in talent identification and talent development in youth soccer in Canada: coaches’ perspectives
Bibliographic record
Abstract
Talent identification and talent development often depend on interconnected variables, such as the athlete’s environment, maturation, the roles of stakeholders, and relationships created and maintained throughout both processes. Talent identification and development require significant resources and finances dedicated to searching for and developing athletes. This qualitative study examines the factors influencing talent identification and talent development processes for youth-aged soccer players in Canada. Insights about talent identification and development were gathered from 16 youth soccer coaches with experiences across different stages of the talent development pathway. Each coach participating in the study took part in a semi-structured interview addressing their awareness of talent identification and talent development processes in Canadian youth soccer, their experiences within its talent development environment, and the challenges/solutions they see to improve the number of talented youth soccer players A thematic analysis process was used to gain insights and identify themes from the interviews. Participants identified different perspectives on talent, talent identification, and talent development. The results of this study demonstrate an inequitable talent identification and development system, which is limited by athletes’ place of birth, socio-economic status, and access to necessary resources. To address the identified constraints, coaches participating in this study recommend: 1) improving access to youth soccer, 2) not driving talent identification and talent development processes on pay-for-play or profit models, 3) prioritizing the social-emotional well-being of children and youth athletes, 4) having a clear definition of talent, talent identification, and talent development, and 5) investing to build system capacity and eliminate inequities.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.031 | 0.010 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".