Examining how learning contexts influence youth's perceptions of life skills development in recreational and competitive sport
Bibliographic record
Abstract
Recently, Pierce, Gould, and Camiré (2017) developed a definition and model detailing individuals' process of learning and subsequent application of life skills (i.e., transfer). Given that the model is new, no empirical research has yet been conducted to test it. As such, the purpose of the present study was to test the Pierce et al. (2017) model, particularly the 'Learning Contexts' component. Fifty-five youth (Mage=14.5, SD=1.74; 17 male, 38 female; 38 competitive, 17 recreational) participated in individual semi-structured interviews to understand their perceptions on life skill development. A deductively oriented thematic analysis (Braun, Clarke, & Weate, 2016) was employed to analyze the data using Pierce et al.'s (2017) model. Findings indicated that the 'Sport Learning Context' and 'Family Learning Context' were the two most prominent settings where life skills development occurred. Differences between the 'Inherent Demands of Sport' and 'Program Design' were acknowledged by the youth. Further, differences were identified in the perceived importance of 'Coaches' Characteristics and Coaching Strategies' across youth from recreational and competitive programs. Practical implications of the study for coaches and program developers are explored. Limitations and future directions are discussed.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".