Urban sustainability via urban productivity? A conceptual review and framework proposal
Bibliographic record
Abstract
This paper offers conceptual and operational insights for more effective and forward-looking local sustainability decision-making through the emerging concept and framework of holistic urban productivity. Following a short discussion of the contemporary understand and theoretical influences of urban sustainability, we explore the concept, principles, and practices of urban productivity and look at how they can help address urban sustainability planning, implementation, and assessment. We then introduce a conceptual framework for holistic urban productivity which encompasses a set of principles and goals to tackle complex urban processes for effective, inclusive, and forward-looking decision-making. It is informed by and converges numerous theories and approaches and seeks to act as an overarching framework to help operationalise sustainability by empowering urban actors to pursue balanced and synergistic optimisation of all urban community elements. A city that implements the principles and framework of holistic urban productivity embraces economic resilience with shifts in employment patterns and habits; innovative, socially just, and environmentally responsible technologies; compact and nature-enhancing land use planning; strong social connections and affordable housing; and green, light, and smart infrastructure. Urban productivity principles can help lead the transformation of cities into well-functioning and sustainable systems.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.016 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".