The reality and context of youth recreational soccer
Bibliographic record
Abstract
While the nature of sport coaching is understood to be unpredictable and complex (Cushion, Armour, & Jones, 2006), it has been suggested that sport be regarded along more specific contextual lines. Within an understanding that allows youth sport to be either recreational, developmental, or elite, (Trudel & Gilbert, 2006) this study investigates the context of youth recreational soccer coaches. Data was collected through an online survey, and 433 recreational coaches from Eastern Ontario completed the survey. Data analysis involved descriptive statistics through SPSS software (IBM SPSS 19). Findings reveal that the context of youth recreational soccer is in itself more complex than we would think. That is, this research demonstrates the heterogeneity within many components such as the quantity and frequency of: games, practices, tournaments, administrative duties and planning, as well as the differences in coach recruitment and certification requirements within this youth sport context. Based on these results, comparisons between the reality of this recreational sport context and the expectations outlined in the Long Term Player Development Model (LTPD) will be drawn. The LTPD is the Long Term Athlete Development model (LTAD) for the sport of soccer and, as a guide, offers specific guidelines about the structure and context of youth soccer. Finally, implications for sport administrators and for those involved with coach development will be outlined in order to address the coaching needs stemming from the diversity within this popular context.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".