Bibliographic record
Abstract
An electronic version of this book is available Open Access at www.tandfebooks.com. It has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives 3.0 license. One of the major challenges of urban development has been reconciling the way cities develop with the mounting evidence of resource depletion and the negative environmental impacts of predominantly urban-based modes of production and consumption. This book aims to re-politicise the relationship between urban development, sustainability and justice, and to explore the tensions emerging under real circumstances, as well as their potential for transformative change. For some, cities are the root of all that is unsustainable, while for others cities provide unique opportunities for sustainability-oriented innovations that address equity and ecological challenges. This book is rooted in the latter category, but recognises that if cities continue to evolve along current trajectories they will be where the large bulk of the most unsustainable and inequitable human activities are concentrated. By drawing on a range of case studies from both the global South and global North, this book is unique in its aim to develop an integrated social-ecological perspective on the challenge of sustainable urban development. Through the interdisciplinary and original research of a new generation of urban researchers across the global South and North, this book addresses old debates in new ways and raises new questions about sustainable urban development. .
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.829 | 0.785 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".