Exploring sport scholars' navigation of their own children's sport participation
Bibliographic record
Abstract
Our recent work (e.g., Larson et al., 2020) suggests a disconnect between research, policy, and practice when it comes to the structure of youth sport and the promotion of long-term athlete development. The purpose of this study was to explore the experiences of reputed sport scholars with their own children's involvement in organized, competitive sport, primarily with respect to the perceived impacts of the amount of time invested in one or multiple sports. We engaged in expert sampling within Canada and the United States and recruited 10 participants, each of whom held a PhD in sport, kinesiology, physical education, or coaching, and had one or more children between the ages of 8-15 years involved in organized, competitive sport. Data were generated through semi-structured interviews via Zoom. Example questions included, What factors do you consider when making decisions about your children's sport and activity schedule? and How have your feelings about what constitutes an ideal pattern of sport participation changed over time? Qualitative content analysis (Elo & Kyngas, 2008) of the transcripts revealed three themes: 1) general feelings about their children's sport schedules, 2) clarifying effects of COVID, and 3) multisport recommendations and reality. Participants acknowledged significant challenges associated with having children involved in more than one organized, competitive sport. As sport scholars, our participants interpreted their experiences with youth sport through the lens of research and evidence on best practices and identified areas for improvement within the current youth sport system, at the levels of policy, clubs, and coaching.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".