Administrator Reflections on Youth Sport Programming: Moving Forward After the COVID-19 Pandemic
Bibliographic record
Abstract
AbstractYouth sport has been a context where positive youth development (PYD) can be promoted (Fraser-Thomas et al., 2005). However, for youth sport to effectively foster PYD, all adult leaders need to understand and invest in the approach. The youth sport context is a large system that encompasses multiple stakeholders across the developmental lifespan of athletes (Dorsch et al., 2022). Much of PYD research reflects the contributions of parents and coaches (e.g., Harwood et al., 2019; Vella et al., 2011), yet often overlooked is the administrator role in PYD. Administrators are tasked with communicating and reinforcing organizational missions across stakeholders for the duration of the season (Schwab et al., 2010). Despite the essential responsibilities administrators hold within the organization, little research has examined how well missions are enacted in youth sport. Within PYD research, studies have found the importance of structuring programs to reach desired outcomes (e.g., life skills; Bean & Forneris, 2016). Thus, it would be beneficial to understand how administrators perceive the missions of, along with the implementation within, youth sport organizations.
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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 | 0.021 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.025 | 0.014 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.014 | 0.036 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".