Risk Management in a Developing Country Context: Improving Decisions About Point‐of‐Use Water Treatment Among the Rural Poor in Africa
Bibliographic record
Abstract
More than 1 billion people, the vast majority of which live in the developing world, lack basic access to clean water for domestic use. For this reason, finding and promoting effective and sustainable solutions for the provision of reliable clean water in developing nations has become a focus of several public health and international development efforts. Even though several means of providing centrally located sources of clean water in developing communities exist, the severity and widespread nature of the water problem has led most development agencies and sanitation experts to strongly advocate the use of point-of-use treatment systems alongside whatever source of water people regularly use. In doing so, however, development practitioners have been careful to point out that any interventions or infrastructure regarding water safety and human health must also adhere to one of the central principles of international development: to facilitate more democratic and participatory models of decision making and governance. To this end, the research reported here focused on the development of a deliberative risk management framework for involving affected stakeholders in decisions about POU water treatment systems. This research, which was grounded in previous studies of structured decision making, took place in two rural villages in the East African nation of Tanzania.
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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 | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".