Youth Leadership Development in the Start2Finish Running & Reading Club
Bibliographic record
Abstract
Researchers have asserted that offering intentional leadership roles to youth can help them to develop life skills (e.g., communication, decision-making); however, few physical-activity-based positive youth development programs provide youth these intentional leadership roles, and little research has explored the impact of these opportunities on youth who take them up. The purpose of this study was to understand the developmental experiences of youth leaders in a physical-activity-based positive youth development program. Sixteen youth leaders (Mage= 13.37, SD = 1.36) from 4 sites of the Start2Finish Running & Reading Club participated in semi-structured interviews to discuss their experiences as junior coaches. Fertman and van Linden’s (1999) model of youth leadership development was used to guide the data collection and analysis. Through deductive-inductive thematic analysis, 3 themes were constructed: (a) awareness: developing into leaders started with seeing potential through role models, (b) interaction: learning by doing and interacting with others helped youth to practice leadership abilities, and (c) mastery: taking on greater responsibility allowed for opportunities to refine leadership abilities and develop a variety of life skills. These themes helped to bring an understanding to the processes involved in leadership and life-skill development. Practical and research implications are discussed regarding leveraging youth leadership opportunities in youth programming.
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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 | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".