Resilient climate urbanism and the politics of experimentation for adaptation
Bibliographic record
Abstract
Cities are increasingly pursuing actions to become more resilient in the face of climate change and to seize related economic growth opportunities. Recent contributions have argued that “climate urbanism” is emerging as a hegemonic trend that is structuring climate adaptation with a focus on the selective securing of vital infrastructure for growth, promoting an apolitical vision of resilience and exacerbating inequalities. Developing situated understandings of these dynamics and their contestation seems key. We analyse this trend of climate urbanism in a specific setting of climate adaptation experiment: living labs. We investigate and intervene on two key processes of the politics of climate experimentation – making for compelling urban projects and focusing on infrastructure reconfiguration – whereby living labs could challenge or, conversely, amplify negative trends of climate urbanism. Our research in Montreal shows the value of understanding the subjectivities of the practitioners involved and the fields of political struggles where adaptation lands. Although the negative trends of climate urbanism appear very resilient and living labs have important limitations, we believe they can be used to muddle through pathways for more debates, equity and justice in climate adaptation.
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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 | 0.014 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.087 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| 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".