Rights, Knowledge, and Governance for Improved Health Equity in Urban Settings
Bibliographic record
Abstract
All three of the interacting aspects of daily urban life (physical environment, social conditions, and the added pressure of climate change) that affect health inequities are nested within the concept of urban governance, which has the task of understanding and managing the interactions among these different factors so that all three can be improved together and coherently. Governance is defined as: "the process of collective decision making and processes by which decisions are implemented or not implemented": it is concerned with the distribution, exercise, and consequences of power. Although there appears to be general agreement that the quality of governance is important for development, much less agreement appears to exist on what the concept really implies and how it should be used. Our review of the literature confirmed significant variation in meaning as well as in the practice of urban governance arrangements. The review found that the linkage between governance practices and health equity is under-researched and/or has been neglected. Reconnecting the fields of urban planning, social sciences, and public health are essential "not only for improving local governance, but also for understanding and addressing global political change" for enhanced urban health equity. Social mobilization, empowering governance, and improved knowledge for sustainable and equitable development in urban settings is urgently needed. A set of strategic research questions are suggested.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".