Poverty reduction through water interventions: A review of approaches in sub‐Saharan Africa and South Asia
Bibliographic record
Abstract
Abstract Water is a key factor in attaining the Sustainable Development Goals (SDGs) of poverty elimination and hunger eradication. The regions of sub‐Saharan Africa (SSA) and South Asia (SA) are stricken with absolute poverty, with 70% of the world's poor. These regions are mainly dependent on agriculture for their livelihood. Diverse rural livelihoods in SSA and SA demand water interventions with more fruitful and effective outcomes in terms of poverty reduction. Existing water resources are not yet fully exploited in SSA and SA as these regions have a significant potential of 43 and 169 million ha, respectively, for irrigated agriculture through various water interventions. Various water interventions to alleviate poverty through better agricultural productivity across SSA and SA have been identified in this study. Major water intervention options identified include actions to: improve rain water management in rain‐fed agriculture, facilitate community‐based small‐scale irrigation schemes, development and management of groundwater irrigation, interventions to upgrade and modernize existing irrigation systems, facilitate and improve livestock production and promote multiple uses of water. Investment in these water interventions will certainly help to break the poverty trap across diverse rural communities of SSA and SA.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".