Border troubles: urban nature and the remaking of public/private divides
Bibliographic record
Abstract
Traditional interventions to “bring nature into the city” were often motivated by a concern to create forms of public space which would provide a public good. Despite such well-intentioned motivations, these public forms of urban nature have always been to some extent bounded, serving some in favor of others, authorizing particular uses and forms of behavior as more or less legitimate, and policing the boundaries of who is/not included in such space. In this paper, we argue that new interventions seeking to bring nature-based solutions (NBS) into the city serve to further trouble these boundaries. NBS seek to use nature to address urban sustainability challenges and they navigate and serve to reconfigure what is (and is not) public in the city. We draw on research undertaken in three cities – Newcastle (United Kingdom), Cape Town (South Africa) and Athens (Greece) to explore the ways in which notions of the private and the public are being remade with and through nature, and its implications for how we might understand urban politics. Our conclusions point to the need for governance arrangements that can support the long-term stewardship of nature in the public interest and with due accountability and we suggest three arrangements.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.052 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".