Active Healthy Kids Global Alliance Global Matrix 4.0—A Resource for Physical Activity Researchers
Bibliographic record
Abstract
BACKGROUND: This brief report provides an overview of the Active Healthy Kids Global Alliance (AHKGA); an introduction to the Global Matrix 4.0; an explanation of the value and opportunities that the AHKGA efforts and assets provide to the physical activity research, policy, practice, and advocacy community; an outline of the series of papers related to the Global Matrix 4.0 in this issue of the Journal of Physical Activity and Health; and an invitation for future involvement. METHODS: The AHKGA was formed to help power the global movement to get kids moving. In 2019-2021, we recruited countries to participate in the Global Matrix 4.0, a worldwide initiative to assess, compare, and contrast the physical activity of children and adolescents. RESULTS: A total of 57 countries/jurisdictions (hereafter referred to as countries for simplicity) were recruited. The current activities of the AHKGA are summarized. The overall findings of the Global Matrix 4.0 are presented in a series of papers in this issue of the Journal of Physical Activity and Health. CONCLUSIONS: The Global Matrix 4.0 and other assets of the AHKGA are publicly available, and physical activity researchers, practitioners, policy makers, and advocates are encouraged to exploit these resources to further their efforts.
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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 | 0.040 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.068 | 0.034 |
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".