Emerging Principles of Healthy Urban Governance
Bibliographic record
Abstract
The world's ever-growing urban settlements will not be salubrious without the consistent adoption and effective implementation of healthy public policies. Urban governance is healthy if it promotes a higher level and fairer distribution of health. In the absence of extensive data linking governance directly to health, the review poses two questions as starting points for further study and action: 1) what can we say about how governance is being used to foster healthy public policy at the local level; and 2) how can forms of governance in multi-level systems be arranged to give municipalities more influence on the upstream factors that determine so many of the problems that they face, as well as their capacity to govern? We begin the paper with a basic framework for governance and its relation to health. We then describe a variety of important governance reforms and innovations that have been deployed to improve various aspects of urban life, such as housing, sanitation, security and economic opportunity. We also discuss general reform measures aimed at promoting good government or civic participation across a range of urban policy areas. With these examples in mind, we address the two key challenges of urban governance for health, extracting from the evidence a preliminary set of principles for effective local governance as well as strategies for projecting city power upstream. We conclude with some observations on the limitations of local governance strategies, topics for further study, and the need for people working in health to clarify the nature of their commitment to good governance. An appendix includes case studies.
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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 | 0.018 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.036 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".