How is water security conceptualized and practiced for rural livelihoods in the global South? A systematic scoping review
Bibliographic record
Abstract
Abstract In the global South, rural and resource-based livelihoods increasingly face water-related risks. The conceptualization and application of the water security concept in relation to rural livelihoods has not been reviewed in this context. To fill this gap, a systematic scoping review of refereed journal articles (2000–2019) was conducted to examine how water security is defined, driven, and addressed for rural livelihoods in the global South. Publications (n = 99) featured diverse methodologies and geographical contexts, and recognized simultaneous drivers of water insecurity and solution strategies for water security. Several shortcomings were evident. First, only 30.3% of publications defined the concept, mostly using frames of ‘adequate’, ‘sufficient’, and ‘acceptable’ water-related risks. Few definitions recognized the role of water security interventions in increasing capabilities and prosperity. Second, technical and managerial responses to proximate drivers of water-related risk – namely climate-related dynamics, water re-allocation, extraction, and mismanagement – outnumbered efforts to identify and transform the underlying social, economic, and political inequities that create and sustain water insecurity. Last, studies focused heavily on agriculture, while labour, transhumance pastoralism, and aquaculture were underrepresented. A research agenda that increases the synergies between the wider water security and rural livelihoods scholarship is advanced to address these shortcomings.
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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 | 0.018 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".