Nature for Resilience? The Politics of Governing Urban Nature
Bibliographic record
Abstract
Transcending initial efforts to make cities “climate smart” by focusing on the potential of new technologies and infrastructural interventions, various actors are increasingly interested in deploying nature to help achieve urban resilience. In this context, rather than taking resilience as a given property of particular systems or entities, it is important to examine why, how, with what implications, and for whom resilience is being enacted. We examine how and why nature-based solutions are being mobilized as a means for governing the resilience of cities and what this means for the ways in which urban resilience is imagined and enacted by different actors. Recognizing that behind different approaches to resilience are diverse ways of valuing nature, we identify four value positions through which nature comes to be understood, given meaning, form, and purpose. Drawing on systematic document analysis and sixty-six interviews from Cape Town, Mexico City, and Melbourne, we discuss how these four value positions of nature are manifested in nature-based interventions for resilience, as well as the implications both for the politics of resilience interventions and the opportunities for enabling social benefit through nature-based solutions. We find that the integration of intrinsic values for nature opens opportunities for nature-based solutions to enable social benefits through an increased focus on the means through which they are implemented. We conclude that urban-nature-as-resilience interventions serve to embed values and the socionatures they produce within the city, creating fundamentally different consequences for the forms and politics of nature-based interventions designed to realize urban resilience.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.045 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".