Sustainability planning, implementation, and assessment in cities: how can productivity enhance these processes?
Bibliographic record
Abstract
Abstract In this “urban century”, planetary realities and increased environmental and social awareness have led to significant international agreements and the recognition that local communities play a crucial role in successfully implementing long-term sustainability goals. Through two case studies in British Columbia, Canada, this research focused on how the concept, principles, and practices of holistic urban productivity can help address urban sustainability planning, implementation, and assessment processes. The research findings showed a range of challenges in urban sustainability such as the persistence on utilitarian approaches to resource management and community planning, the prioritization of short-term policies, a general resistance to systemic thinking, and various shortfalls in municipal capacity. These obstacles reflected the reality and complexity of urban sustainability processes and highlighted the need to redesign current decision-making. Addressing issues that transcend humanmade borders requires new configurations, non-hierarchical decision-making processes, and using local knowledge as a key guiding tool. Our recommendation is that cities embrace systems thinking in sustainability planning and implementation by focusing more on holistic evaluation of policy impact and finding synergies among policies and stakeholders in all sectors.
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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 | 0.047 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.023 | 0.012 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".