The CUSSH programme: learning how to support cities’ transformational change towards health and sustainability
Bibliographic record
Abstract
(CUSSH) project will develop critical evidence on how to achieve the far-reaching transformation of cities needed to address vital environmental imperatives for planetary health in the 21st Century. CUSSH's core components include: (i) a review of evidence on the effects of climate actions (both mitigation and adaptation) and factors influencing their implementation in urban settings; (ii) the development and application of methods for tracking the progress of cities towards sustainability and health goals; (iii) the development and application of models to assess the impact on population health, health inequalities, socio-economic development and environmental parameters of urban development strategies, in order to support policy decisions; (iv) iterative in-depth engagements with stakeholders in partner cities in low-, middle- and high-income settings, using systems-based participatory methods, to test and support the implementation of the transformative changes needed to meet local and global health and sustainability objectives; (v) a programme of public engagement and capacity building. Through these steps, the programme will provide transferable evidence on how to accelerate actions essential to achieving population-level health and global climate goals through, amongst others, changing cities' energy provision, transport infrastructure, green infrastructure, air quality, waste management and housing.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.053 | 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".