Climate Shocks and Local Urban Conflicts: An Evolutionary Perspective on Risk Governance in Bhubaneswar
Bibliographic record
Abstract
In this paper, we explore the complex entanglements between ongoing land conflicts and climate shocks, and their implications for risk governance paths and evolution. We focus on ways in which concepts of shock and conflict can be incorporated into social–ecological systems thinking and applied to risk governance practice in a southern cities context. Through a qualitative inquiry of two slum redevelopment projects in Bhubaneswar city in India, we trace the origin and evolution of conflict around land tenure and eviction in informal settlements, as well as its interaction with local manifestations of climate shocks. Climate policies, as responses to climate shock and intended to mitigate climate risk, are observed as constructed, interpreted, framed, and used strategically by formal actors to further urban development objectives, while the local knowledge systems, risk perceptions, and adaptations are ignored in practice. This study helps to re-think the complexities of climate risk governance in southern urban spaces where multiple risks overlap and interact within the diverse realities of informality and vulnerability. A singular focus on one type of risk, on the formal order to manage that risk, is likely to overlook other risks and opportunities. Hence, shocks are likely to produce more unanticipated effects, conflicts function as the unobserved middle term, and the formal policies and plans to mitigate climate risk contribute to the creation of new risk.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".