A Review of Current Evaluation Urban Sustainability Indicator Frameworks and a Proposal for Improvement
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
This paper addresses the link between data, metrics, and the paths from cause to effect in urban sustainability and livability frameworks. The first section thoroughly discusses the different existing frameworks for evaluating sustainability and livability goals for urban communities. In the results section, a qualitative and quantitative analysis of a comprehensive list of frameworks that evaluate sustainability and livability in cities is elaborated, with a thorough post-process of the different schemes from an epistemological perspective to analyze the subjectivities implicit in any urban-level sustainability framework. Finally, in the discussion section, two main aspects are tackled. The first is the development of a proposal for a set of indicators that incorporates the best of the different frameworks analyzed. The second aspect deals with the methodology of implementation of these frameworks. Here, the authors point out the weaknesses of current urban-level sustainability frameworks and their main components, and they propose a set of criteria to overcome the different detected gaps. All these steps have helped the authors establish a clear roadmap for developing the platform TOOLS4Cities that can help set a future reference methodology for urban sustainability evaluation.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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