Turning up the heat : Urban political ecology for a climate emergency
Bibliographic record
Abstract
Since its emergence in the 1990s, the field of Urban Political Ecology (UPE) has focused on unsettling traditional understandings of the 'city' as entirely distinct from nature, showing instead how cities are metabolically linked with ecological processes and the flow of resources. More recently, a new generation of scholars has turned the focus towards the climate emergency. Turning up the heat seeks to turn UPE's critical energies towards a politically engaged debate over the role of extensive urbanisation in addressing socio-environmental equality in the context of climate change. The collection brings together theoretical discussions and rigorous empirical analysis by key scholars spanning three generations, engaging UPE in current debates about urbanisation and climate change. Engaging with cutting edge approaches including feminist political ecology, circular economies, and the Anthropocene, case studies in the book range from Singapore and Amsterdam to Nairobi and Vancouver. Contributors make the case for a UPE better informed by situated knowledges: an embodied UPE that pays equal attention to the role of postcolonial processes and more-than-human ontologies of capital accumulation within the context of the climate emergency. Acknowledging UPE's rich intellectual history and aiming to enrich rather than split the field, Turning up the heat reveals how UPE is ideally positioned to address contemporary environmental issues in theory and practice.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.032 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| 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".