Research for City Practice
Bibliographic record
Abstract
CITY KNOW-HOWPlanetary health and human health are influenced by city lifestyles, city leadership, and city development. Changing the trajectory requires concerted action, and the journal Cities & Health journal is dedicated to supporting the flow of knowledge, in all directions to help make this happen. We are dedicated to supporting communication between researchers, practitioners, policy-makers, communities and decision-makers in cities. The aim of the City Know-how section of the journal is to make research accessible to all, explaining the key messages to and for city leaders, communities and all those professions involved in city policy and practice. In response we would like to hear more about research priorities from those most closely connected with supporting health and health equity through everyday urban lives.
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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 | 0.134 | 0.240 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.011 | 0.025 |
| Scholarly communication | 0.028 | 0.027 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.011 | 0.017 |
| Insufficient payload (model declined to judge) | 0.045 | 0.012 |
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".