Bibliographic record
Abstract
This article identifies and explains an underlying transition in global urban policy and discourse from the city as a sustainability problem to the city as a sustainability solution. We argue that contemporary policy discourses of cities saving the planet should be understood in the context of three major historical developments which have their roots in the 1970s and which intensified throughout the 1990s. The first is sprawl: the urban sustainability policy agenda in the Global North has been in large part a reaction to several decades of urban expansion and car-based planning. The second is informal settlements: since the introduction of UN-HABITAT in 1978, an international policy agenda has formed around addressing the environmental deficits associated with processes of informal urbanisation above all in the Global South. And the third is climate change, as the overarching concern that connects urban-environmental problems and policies in the North and South. We then contextualise the articles in this special issue by outlining a new research agenda for decoding the notion that cities can save the planet, which emphasises the need for an historical, multi-spatial, political and representational analysis of urban sustainability thinking and policy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.028 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".