Converging Urban Agendas: Toward Healthy and Sustainable Communities
Bibliographic record
Abstract
In light of recent developments such as the COP21 Paris climate agreement, the UN adoption of the Sustainable Development Goals for 2030, and the Habitat III Conference, there is increasing recognition of the role of human settlements as key components of both global challenges and global solutions. “Urban sustainability” under various names has matured over the last three decades not only in planning and related fields, but also in wider professional and popular discourse. In this paper we trace a historical overview of urban sustainability theory and practice, and explain why urban sustainability planning and development currently face limited and inconsistent application. We show that this lack of public uptake is due in part to monitoring, assessment, and decision-support frameworks and tools that do not engage citizens and their governments in a shared “strong sustainability” analysis and/or vision. We argue that urban sustainability today clearly needs to embrace equity, inclusion, and other social considerations; contribute to constructive societal mobilisation and compelling policy-making; advocate for development as a better alternative to growth; encourage the integration of human and environmental health interests; and encompass triple-bottom-line-inspired outcomes. Focusing on community capital productivity and regeneration may be the key to advancing healthy and sustainable communities.
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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 | 0.022 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.021 | 0.045 |
| Scholarly communication | 0.026 | 0.031 |
| Open science | 0.004 | 0.058 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".