Bibliographic record
Abstract
The study focuses on the problems of water allocation in Thailand where the alternative of introducing a comprehensive water-pricing system has become a controversial issue. Governed by the regime of open access, the Thai water allocation system has failed to cope with newly surfacing demands and necessities. The system has yet to overcome old perceptions and habits. The case studied is the Bangkok Metropolitan Region (BMR), the area of greatest social-economic changes and most acute water allocation conflicts in Thailand. Proposals to shift water allocation decisions from the government to the market by way of water-pricing have not been well received by the government as a result of political uncertainty. While the government is in favour of increased centralization to improve its administrative control, the academic circles are calling for institutional reforms which would include specifications of property rights to water. As a contribution to the area of Thai water management, this study examines water resource issues in relation to the water demand profile of the BMR, the issue of property rights and water allocation, the historical development within Thai political economy, the trends that shape possible changes within the policy arena and how these changes can positively affect water allocation systems in Thailand in terms of pricing. The study is based on reviews of relevant theory, interviews and analysis of both published and unpublished data on the BMR. Water issues in the BMR revolve around problems of water shortage, groundwater over-extraction, and deteriorating water quality. The demand profile indicates that the existing water allocation system is not able to sufficiently accommodate the BMR's water demand, neither in quantity nor in spatial distribution. The state's centralized administrative control has failed to provide an efficient and equitable system for water allocation. Solutions currently being proposed follow one of three alternatives: increase state centralization to correct administrative errors, conserve and revive traditional systems, or introduce market-based tools such as a water-pricing. The study suggests that there needs to be a shift in government policy to develop a mechanism whereby water allocation and management recognize the significance of local community inputs as represented by interest groups.
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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 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".