The PAICE project: Integrating health and health equity into UK climate change policy
Bibliographic record
Abstract
This paper announces a new initiative - the research project Policy and Implementation for Climate & Health Equity (PAICE), which aims to investigate the complex systemic connections between climate change action, health and health equity, for translation of evidence into policy and practice in the UK. Using transdisciplinary approaches, PAICE will: (1) co-develop a programme theory and linked monitoring and evaluation plan, (2) work with the UK Climate Change Committee (CCC) and the Greater London Authority (GLA) using system dynamics to analyse national and local policy opportunities, (3) build an integrated model of the effects of these policies on population health, health equity and greenhouse gas emissions, (4) apply the findings to the CCC monitoring framework and GLA policy development, and (5) use the programme theory to help evaluate achievement of PAICE processes and objectives. If successful, PAICE will have helped to establish a systems capability to (i) monitor whether Government plans are on track to deliver their climate targets and associated health impacts and (ii) understand how relevant policy and implementation approaches could be enhanced.
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 | 0.018 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.034 | 0.002 |
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".