Development and application of a multi-scalar, participant-driven water poverty index in post-tsunami India
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article presents a modified water poverty index that captures several waterscape attributes to better understand complex issues surrounding water. Household surveys (n = 300), water quality tests (n = 375) and qualitative methods were deployed to examine 14 post-tsunami settlements in Nagapattinam and Karaikal Districts (India) through the lens of water. Data were used to develop a contextualized, participant-driven water poverty index to measure water poverty at several scales. Statistical tests revealed significant differences between the two districts (p ≤ .0001) and between rural and urban areas within each district (p ≤ .0001). Three weight schemes (one dictated entirely by research participants) produced analogous outcomes though predicated on different indicator arrangements.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it