Public Investment on Irrigation and Poverty Alleviation in Rural Laos
Bibliographic record
Abstract
The demand for water is rising rapidly, particularly in agricultural and environmental sectors. This has led to more competition to access limited and scarce water resources. Therefore, choosing an appropriate approach to manage water resources, distribution and allocation, to attain sustainable agriculture is critical for every country worldwide. The most well-known method to preserve or store water and adaptation strategy to climate change is irrigation. This paper wished to understand the impact of irrigation on farmers’ income in Laos, especially from rice, which is the main crop of rural people. The difference in differences (DID) method was employed to estimate the regression results. The DID was estimated by the pooled OLS of the effect on the log of households’ rice farm income and log of households’ total income with household head’s age, education, gender, household size, ethnicity and harvest areas variables pointing out the coefficients of the outcome variables of interest (after treatment) were 0.037 and 0.076 with positive sign but statistically insignificant. The result implies irrigation has no impact on rice products. In other words, irrigation does not increase households’ income. The finding indicates the type of irrigation, the location of the operation headquarters and the management system or governance are crucial factors for explaining the impact of irrigation on the rice products in Laos.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".