A Review of Current Evaluation Urban Sustainability Indicator Frameworks and a Proposal for Improvement
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.
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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 | 0.027 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.020 | 0.025 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".