Bibliographic record
Abstract
With the worldwide rise in noncommunicable disease, physical inactivity, obesity, and the global presence of Adverse Childhood Experiences (ACEs), health and sport science practitioners must be able to address each of these health domains while considering frameworks for the most urgent health and human development priorities in a sustainable manner. The sector of sport for development, which uses physical activity, sport, and game-based programming to address specific development and peace initiatives to empower individuals and communities, is one such approach that practitioners can employ to address such challenges. During the 2000-2015 era of the United Nations (UN) Millennium Development Goals (MDGs), the sport for development sector used sport to address several MDGs, contributing most significantly towards improving HIV/AIDS knowledge, attitudes, and behavior changes. Practitioners are still using sport to address the 2015-2030 UN Sustainable Development Goals (SDGs). This article explores case studies of 17 sport for development initiatives that are meeting key targets for each of the 17 SDGs. Furthermore, it provides recommendations for how to further advance sport for development’s contributions. By synthesizing cost effective analyses and discussing key components to further the sport for development field, this article maps a way forward to advance sport for development as a cost-effective and viable tool for addressing the SDGs, reducing the effects of unresolved ACEs, and promoting physical activity to help individuals and communities lead healthy, empowered lives.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".