Water demand management in Yemen and Jordan: addressing power and interests
Bibliographic record
Abstract
This paper investigates the extent to which entrenched interests of stakeholder groups both maintain water use practice, and may be confronted. The focus is on the agricultural sectors of Yemen and Jordan, where water resource policymakers face resistance in their attempts to reduce water use to environmentally sustainable levels through implementation of water demand management (WDM) activities. Some farmers in both countries that have invested in irrigated production of high-value crops (such as qat and bananas) benefit from a political economy that encourages increased rather than reduced water consumption. The resultant over-exploitation of water resources affects groups in unequal measures. Stakeholder analysis demonstrates that the more ‘powerful’ groups (chiefly the large landowners and the political elites, as well as the ministries of irrigation over which they exert influence) are generally opposed to reform in water use, while the proponents of WDM (e.g. water resource managers, environmental ministries and NGOs, and the international donor community) are found to have minimal influence over water use policy and decisionmaking. Efforts and ideas attempted by this latter group to challenge the status quo are classified here as either (a) influencing or (b) challenging the power asymmetry, and the merits and limits of both approaches are discussed. The interpretation of evidence suggests current practice is likely to endure, but may be more effectively challenged if a long-term approach is taken with an awareness of opportunities generated by windows of opportunity and the participation of ‘overlap 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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".