Urban Water Crises under Future Uncertainties: The Case of Institutional and Infrastructure Complexity in Khon Kaen, Thailand
Bibliographic record
Abstract
This paper uses the emerging crises in water management in North East Thailand as a case study to examine the effectiveness of existing institutional structures and processes to adapt to an uncertain future climate. We argue that it is through an analysis of the interface of actors, institutions and physical infrastructure that climate vulnerability can be better understood, and conversely, that climate resilience might be strengthened. This research has global significance as case studies of emerging water crises provide valuable insights into future vulnerabilities and the Thailand experience speaks to similar challenges across the global South. Our findings illustrate that water managers on the front line of dealing with climate variability are constrained by the interaction of infrastructure that was designed for different times and needs, and of institutional structures and processes that have emerged through the interplay of often competing organisational remits and agendas. Water management is further constrained by the ways in which information and knowledge are generated, shared, and then applied. Critically the research finds that there is no explicit consideration of climate change, but rather universally-held assumptions that patterns of water availability will continue as they have in the past. As a result, there is no long-term planning that could be termed adaptive, but rather, a responsive approach that moves from crisis to crisis between seasons and across years.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".