Emerging Livelihood Vulnerabilities in an Urbanizing and Climate Uncertain Environment for the Case of a Secondary City in Thailand
Bibliographic record
Abstract
Increasing flood risks in Thailand are leading to new challenges for flood management and subsequently for livelihoods, which are still significantly agricultural. Policy makers prefer building flood protection infrastructure over utilizing non-structural measures like urban planning regulations to mitigate risks. We argue that unplanned urbanization intensifies flood risks and livelihood vulnerability and may even create new poverty patterns in peri-urban areas. However, urbanization can also strengthen the adaptive capacity of people in flood risk areas by providing more secure employment opportunities. We assess the livelihood vulnerability of Pra Lab, a peri-urban area of Khon Kaen City in Northeast Thailand, using a qualitative and quantitative analysis. The study relies on a vulnerability index developed from a household survey and rainfall statistics, complemented by household in-depth interviews. We further identified factors of unplanned urbanization in Khon Kaen City and Pra Lab through interviews with relevant local government offices. Our findings show that Pra Lab’s household livelihoods are moderately vulnerable to flood due to high financial (i.e., income, debts) and physical vulnerability (i.e., housing, urban systems, infrastructure). Major factors of unplanned urbanization that contribute to flood risks are lack of land use regulations, inefficient monitoring of land and house elevations, reduced pervious surfaces, ineffective water governance and insufficient wastewater treatment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".