Bibliographic record
Abstract
The critical considerations in this commentary have been stimulated by the articles joined together in this inspiring collection. Specifically, this commentary reflects on how one might imagine an urban political ecology for the age of planetary urbanisation. While the editors of and contributors to this special issue have done an admirable job of providing intellectual coherence to this project, there remains work to do, especially on the conceptual and theoretical front. The conveners of this symposium lay out an ambitious agenda for the papers in this issue and ultimately for the field: They ask: ‘why does everyone think cities can save the planet?’. It is part real inquiry, part rhetorical question. These questions also provide the entry point into a coherent and serious theoretical project that lies at the bottom of the assembled papers here and is elegantly laid out by the special issue editors in their introduction. This commentary takes up the challenges posed by the theoretical and empirical projects discussed in this issue and discusses them in light of past advances in thinking across the city–nature divide, technological politics and the changing spaces and times of current urbanisation.
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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.021 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.009 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 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".