Planning for global environmental change in Bangkok's informal settlements
Bibliographic record
Abstract
Government agencies in cities across Asia recognise that municipalities must take steps to adapt to projected climate changes if people and places are to be kept above water. This paper focuses on planning for climate change in Bangkok because it ranks among the top 10 port cities vulnerable to climate change related flooding. It is also understood that the most devastating impacts of climate change will be suffered by the city's most vulnerable residents: the poor. Not only do impoverished people occupy physically vulnerable space, such as riverbanks, but they are also the least equipped to recover from the disruption of their livelihoods.Several scholars have identified “institutional traps” that prevent the Thai government from successfully aiding poor and marginalised flood victims in the past. These include poor coordination, lack of monitoring and evaluation, rigidity, crisis management and elite capture. Lebel, Manuta, and Garden (2011, 56) Lebel, L., J.B. Manuta, and P. Garden. 2011. “Institutional Traps and Vulnerability to Changes in Climate and Flood Regimes in Thailand.” Regional Environmental Change 11 (1): 45–58.[Crossref], [Web of Science ®] , [Google Scholar] pose the crucial question: “How have individuals – from local community leaders through to national level politicians and bureaucrats – successfully influenced policy and programmes to avoid institutional traps and improve adaptive capacities to climate change?”In this paper, we begin to address this question through examining emergent methods of “community based adaptation” and reviewing case studies of adaptation action from other vulnerable communities in the Global South. These lessons – such as overcoming institutional rigidity and avoiding elite capture – are important for Bangkok and other cities in the Global South that face many different challenges by global environmental change.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".