Choosing indicators to evaluate Healthy Cities projects: a political task?
Bibliographic record
Abstract
Ever since their beginning in 1986, Healthy Cities projects all over the world have been confronted with the issue of evaluation. However, after 20 years, many key dilemmas constantly reappear, people often looking for a kind of 'magic' list of universally applicable indicators to evaluate these initiatives. In this article we address five questions, allowing to illustrate the evaluative dilemmas the Healthy Communities movement is confronted with: Why evaluate Healthy Cities? What should be evaluated? Evaluate for who? Who should undertake the evaluation? How should the evaluation be performed? We conclude by formulating three recommendations in order to stimulate exchanges and debate. Our argument is based on a recent thorough analysis of the evaluative literature pertaining to the Healthy Cities movement, as well as on two decades of reflection on and involvement with this issue locally, nationally and internationally.
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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 | 0.277 | 0.261 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.009 | 0.022 |
| Scholarly communication | 0.029 | 0.024 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.010 | 0.018 |
| Insufficient payload (model declined to judge) | 0.004 | 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".