Behavior Change in Water, Sanitation, and Hygiene: A 100-Year Perspective
Bibliographic record
Abstract
Abstract The current methodological paradigm for addressing water, sanitation, and hygiene (WaSH) inaccessibility in rural sub-Saharan Africa is achieving insufficient progress. This essay evaluates WaSH-related policy, programming, and discourse from 1918 to 2021 to identify how this paradigm evolved and how it may reform. I argue that political–economic environments have strongly influenced existing sectoral praxis, shaping both programmatic methods and means. Colonial occupations generated rural–urban material inequalities, which were maintained and exacerbated during postwar reconstruction (1950–1970) and the proliferation of neoliberalism (1970–1990s). Meanwhile, modernization theory, a fundamental feature of colonial thought, has persisted discursively and practically. That is, rural resource limitations led WaSH practitioners to promote lower-cost appropriate technologies in the 1980s. Then, with challenges regarding technological disuse and misuse, behavior change–oriented methodologies responsively emerged in the 2000s and continue today. Yet, much like colonial predecessors, this latter turn presupposes that its programmatic benefactors must adapt to access WaSH services. Behavior change programs thus fail to critically consider the role of technological inadequacies and associated risk exposures in perpetuating existing inequities. Investigation of utility-style service models, where WaSH services adapt to the lives of its benefactors and behavioral persuasion is substituted for nonuser technological management, is recommended.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".