Score! Using technology to deliver positive youth sport programs
Bibliographic record
Abstract
While the physical benefits of sport participation are clear, the psychosocial outcomes of participation are not as well established. Positive youth development (PYD) has advanced the idea that youth are resources to be cultivated; the development of young people involves fostering positive outcomes rather than simply reducing problem behaviors (Benson et al., 2006). Research points to the potential of youth sport as an avenue to support the growth of particular assets and outcomes (MacDonald et al., 2011; Strachan et al., 2009). A recurring theme in this line of research, however, is the need to establish deliberate delivery so that positive outcomes are more likely. The purpose of the project is to design and deliver an innovative, technology-based PYD program. The SCORE! (Sport COnnect and REspect) program has been established to deliver a PYD program that supplements participation in an organized sport setting (www.projectscore.ca). Five coaches followed a 10-lesson program with a variety of youth sport teams and were interviewed upon completion. Feedback from the coaches indicated that the lessons were appropriate. Constructive comments regarding the ease of the website and specific sessions (i.e., "Your turn") were noted. Information will be used to evaluate and/or modify the program prior to its implementation in the larger study. Results will have a direct impact on youth and coaches alike; young people will learn valuable psychosocial skills while enhancing sport competence and participation while coaches will gain knowledge that will assist with their coaching development and create positive sport contexts for children and youth.Acknowledgments: SSHRC
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".