Positive youth development in the context of organized sport and deliberate play
Bibliographic record
Abstract
The positive youth development literature provides a number of different frameworks that can be used to conceptualize the "development of athletes." In particular, the 5Cs – Competence, Confidence, Connection, Character, and Caring/Compassion (Lerner, Fisher, & Weinberg, 2000) can be hypothesized as desirable outcomes that should emerge from regular participation in sport. Côté and colleagues (Côté, Bruner, Erickson, Strachan, & Fraser-Thomas, 2010) recently reviewed the sport literature and proposed collapsing the 5Cs into 4Cs (Competence, Confidence, Connection, Character/Caring). This step was taken given the frequent integration of caring/compassion within the character development literature in sport (e.g., Shields & Bredemeier, 1995) and the overlap between these three constructs (i.e., character, caring, and compassion). The 4Cs represent a promising framework to conceptualize and examine youth development in organized and non-organized (i.e., deliberate play; Côté, 1999; Côté, Baker, & Abernethy, 2007) sport settings. This presentation will focus on the benefits of using the 4Cs to evaluate the impact of sport participation on youth development. Specifically, the two different contexts of youth sport participation that differ in the degree of youth and adult influence, organized sport and deliberate play, will be contrasted as potentially providing distinct developmental experiences.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".