Using positive youth development through sport to promote the United Nations Millenium Development goals
Bibliographic record
Abstract
In 2000, the United Nations (UN) hosted a world summit to set their major goals and objectives. This summit produced the UN Millennium Development Goals (MDGs), a set of eight quantified goals that addressed key social problems in the world. The UN has recognized the potential of sport and physical activity settings for addressing the MDGs. The coach plays the primary role in shaping the youth sport environment, and consequently has a major impact on the quality of youths' experience in sport. Framed around the principles of positive youth development, this study developed and implemented sport-related activities which addressed the UN MDGs of health, education, and empowering women. Participants included six youth sport coaches from both recreational and competitive leagues. Multiple methods were used to collect data. Coaches perceived the project as successful and the activities were seen as beneficial for athletes and for the team. In particular, the coaches believed the activities improved the athletes' values towards health, education, and empowering women. In addition, coaches believed the activities increased team cohesion and their players showed more caring, compassion, and character (indicators of PYD). Overall, results demonstrated that typical youth sport settings can be used to teach citizenship skills and promote PYD.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".