Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it