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.Global Suburbanisms funded the research for a paper, with the lead authors Yannis Tzaninis and Tait Mandler: Tzaninis, Y., Mandler, T., Kaika, M., and Keil, R. (2021).Moving urban political ecology beyond the 'urbanization of nature', Progress in Human Geography, 45(2): 229-52.This paper also provided the basis for the Introduction, and eventually also the Epilogue of this current book.The paper initially provided the impetus for the Sub/ urban Political Ecology Workshop, also funded and co-organised to great extent by Global Suburbanisms, and co-organised and hosted by the University of Amsterdam in February 2019.We would like to specifically thank Cara Chellew and Lucy Lynch at York and Yannis Tzaninis in Amsterdam for their tireless efforts to make the event a huge success.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.431 | 0.355 |
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".