Perspective: The Importance of Water Security for Ensuring Food Security, Good Nutrition, and Well-being
Bibliographic record
Abstract
Water security is a powerful concept that is still in its early days in the field of nutrition. Given the prevalence and severity of water issues and the many interconnections between water and nutrition, we argue that water security deserves attention commensurate with its importance to human nutrition and health. To this end, we first give a brief introduction to water insecurity and discuss its conceptualization in terms of availability, access, use, and stability. We then lay out the empirical grounding for its assessment. Parallels to the food-security literature are drawn throughout, both because the concepts are analogous and food security is familiar to the nutrition community. Specifically, we review the evolution of scales to measure water and food security and compare select characteristics. We then review the burgeoning evidence for the causes and consequences of water insecurity and conclude with 4 recommendations: 1) collect more water-insecurity data (i.e., on prevalence, causes, consequences, and intervention impacts); 2) collect better data on water insecurity (i.e., measure it concurrently with food security and other nutritional indicators, measure intrahousehold variation, and establish baseline indicators of both water and nutrition before interventions are implemented); 3) consider food and water issues jointly in policy and practice (e.g., establish linkages and possibilities for joint interventions, recognize the environmental footprint of nutritional guidelines, strengthen the nutrition sensitivity of water-management practices, and use experience-based scales for improving governance and regulation across food and water systems); and 4) make findings easily available so that they can be used by the media, community organizations, and other scientists for advocacy and in governance (e.g., tracking progress towards development goals and holding implementers accountable). As recognition of the importance of water security grows, we hope that so too will the prioritization of water in nutrition research, funding, and policy.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".