Choosing indicators to evaluate Healthy Cities projects: a political task?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it