If we build it, who will come? The case for attention to equity in healthy community design
Bibliographic record
Abstract
The health and economic burden of physical inactivity is substantial and showing few signs of decline despite increased attention in policy agendas globally.1 To address this pandemic, we need to look beyond the health sector and reimagine our environments into places that get us moving more, and sitting less. Governments are investing in active transportation and sustainable development projects in light of their health, economic and environmental promise. But who stands to benefit from these investments, and in what context? Physical inactivity is socially distributed, varying by socioeconomic status, gender, age, education and ethnicity.2 This trend is global and consequential; a recent study using smartphone data from over 700 000 people across 111 countries showed that inequities in physical activity are a better predictor of obesity prevalence than average physical activity.3 The built environment has the potential to either mitigate or exacerbate health inequities. Vulnerable populations often live in built environments lacking access to amenities that promote active mobility, such as transit and recreational facilities.4 In addition to supporting physical activity, designing safe environments that increase the active transportation of underserved groups may …
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.
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 | 0.013 | 0.056 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.019 | 0.040 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".