Bibliographic record
Abstract
This book has long-and medium-term histories that have accumulated multiple layers of intellectual, institutional, and personal indebtedness that we can only acknowledge here briefly.Among the long histories is certainly the collaboration of Kaika and Keil on matters urban and nature that goes back to the 1990s.Most of our close co-conspirators and comrades from the early period of UPE are in this book and we are grateful to them for allowing us to share a path in critical solidarity over those years and decades in creating one of the most rewarding, inspiring, and productive projects of our careers.It is the now time-honoured collective project of attempting to understand the urbanisation of nature that we acknowledge here as the seedbed for this particular contribution we have put between these covers.Among the more short-term histories that need mentioning here is the opportunity that arose at the end of the Major Collaborative Research Initiative on Global Suburbanisms, sponsored by Canada's SSHRC, and housed at York University, to look specifically at the intersection of global suburbanisation -or as we would also call it, extended urbanisation -and urban political ecologies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".