Launch of new series: bright spots, physical activity investments that work
Bibliographic record
Abstract
The technological, economical and labour trends that practically eliminated the need for daily physical activity in much of the developed and increasingly in the developing world occurred far too rapidly for human physiology to efficiently adapt. The result is the escalating epidemic of chronic diseases that are contributed to or amplified by physical inactivity. Re-engineering the world to help us prioritise physical activity presents many challenges. There is no single solution or ‘silver bullet’. Understanding a complex problem like physical inactivity requires interdisciplinary thinking and appreciation of both the local and global context. What is clear is that tackling physical inactivity requires a multisectoral response that goes well beyond the traditional healthcare settings and involves, for example, government entities, non-governmental organisations, and private industry. Launched in 2012, the Investments that Work for Physical Activity initiative,1 builds on the 2010 Toronto Charter for Physical Activity …
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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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.019 | 0.023 |
| Insufficient payload (model declined to judge) | 0.026 | 0.020 |
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".