A declaration on the value of experiential measures of food and water insecurity to improve science and policies in Latin America and the Caribbean
Bibliographic record
Abstract
BACKGROUND: Water security is necessary for good health, nutrition, and wellbeing, but experiences with water have not typically been measured. Given that measurement of experiences with food access, use, acceptability, and reliability (stability) has greatly expanded our ability to promote food security, there is an urgent need to similarly improve the measurement of water security. The Water InSecurity Experiences (WISE) Scales show promise in doing so because they capture user-side experiences with water in a more holistic and precise way than traditional supply- side indicators. Early use of the WISE Scales in Latin American & the Caribbean (LAC) has revealed great promise, although representative data are lacking for most of the region. Concurrent measurement of experiential food and water insecurity has the potential to inform the development of better-targeted interventions that can advance human and planetary health. MAIN TEXT: On April 20-21, 2023, policymakers, community organizers, and researchers convened at Universidad Iberoamericana in Mexico City to discuss lessons learned from using experiential measures of food and water insecurity in LAC. At the meeting's close, organizers read a Declaration that incorporated key meeting messages. The Declaration recognizes the magnitude and severity of the water crisis in the region as well as globally. It acknowledges that traditional measurement tools do not capture many salient water access, use, and reliability challenges. It recognizes that the WISE Scales have the potential to assess the magnitude of water insecurity more comprehensively and accurately at community, state, and national levels, as well as its (inequitable) relationship with poverty, poor health. As such, WISE data can play an important role in ensuring more accountability and strengthening water systems governance through improved public policies and programs. Declaration signatories express their willingness to promote the widespread use of the WISE Scales to understand the prevalence of water insecurity, guide investment decisions, measure the impacts of interventions and natural shocks, and improve public health. CONCLUSIONS: Fifty-three attendees endorsed the Declaration - available in English, Spanish and Portuguese- as an important step to making progress towards Sustainable Development Goal 6, "Clean Water and Sanitation for All", and towards the realization of the human right to water.
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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 | 0.036 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.017 | 0.028 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".