People and politics: Urban climate resilience in Phnom Penh, Cambodia
Bibliographic record
Abstract
The rapid growth Cambodia has experienced over the past two decades has resulted in a dramatic transformation of its built environment, in particular, its largest city, Phnom Penh. The shape this urban development has taken echoes that of many developing countries whose urban landscape features gleaming skyscrapers, apartment buildings, and edge-city projects spread across a rapidly expanding urban area. Such a pattern of urbanization is occurring in Phnom Penh while the city faces increased flooding, lack of adequate urban infrastructure, and vulnerability to impacts of climate change. At the same time, embedded within national policy discourses of climate change and social/economic planning, and backed by international donors, are calls for strengthening or developing resilience. Yet, in the city there are signs of land dispossession, marginalization, inequality, and exacerbated poverty. In parallel to high-level discourses of urban resilience, on the ground there have been “everyday forms of resilience” that show how people enact and build resilience through collective action and advocacy for the rights of the urban poor. In reconciling this dichotomy, we argue that the continued reproduction of a technocratic-focused discourse on resilience in Cambodia by national and international actors overshadows the everyday contestations, strategies and resilience-making practices of people in urban areas. Through three examples, we showcase the varying ways in which these contestations and strategies occur in, and despite, an environment of suppression, and how they are challenging the status quo. In doing so, we shed light not only on the politics of resilience but, more importantly, the implications of the political agendas that ultimately contribute to exacerbating vulnerabilities of urban residents, even as calls continue for increased urban “resilience.”
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".