Nazava Water Filters: Social Impact Assessment
Bibliographic record
Abstract
Nazava sells its water filters to create social impact as measured by improved health, monetary savings, time savings and greenhouse gas emission reductions. In order to validate this impact model, two Global Social Benefit Fellows conducted research from June to August 2016 across three islands in Indonesia, to gather data from 87 participants, 70 of whom completed a semi-structured interview and mobile survey while the remaining 17 completed a semi-structured interview only. The research team conducted interviews to gather qualitative information on the social impact metrics in Sabu and Kupang. During this time, the mobile survey was pilot tested with 11 participants in Sabu and Kupang so that it could be revised for clarity and accuracy. More interviews and the revised survey were completed with the remaining participants on Java Island, which allowed for more qualitative and quantitative data collection. This social impact assessment details the quantitative and qualitative findings on health benefits, monetary savings, and time savings as concluded from the survey and interview data. The study indicates that health benefits are more evident in remote and isolated communities such as Sabu Island where purified water is hard to access and the value of purification is not well understood. Across our study we found that the Nazava users surveyed saved on average 2 hours and 40 minutes per week on water purification activities after purchasing the filter. This represents an approximately 60 percent reduction in time spent on the process of obtaining purified water. Similarly, quantitative data gathered through the mobile survey indicates that Nazava users surveyed saved on average 22,000 Indonesian Rupiah (US $1.71) per week on water purification materials, a decrease of approximately 50 percent. Lastly, other factors that beneficiaries reported during semi-structured interviews demonstrated additional impacts on general quality of life for filter users. This report presents an analysis of Nazava's impact model concerning time savings, monetary savings and health benefits. It analyzes the depth of impact Nazava filters are having on beneficiaries in regards to each of these dimensions across a variety of geographic locations.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".