Millennial Policies and Strategies for Promoting Household Water Security: A Southern African Example
Bibliographic record
Abstract
The development record of water supply delivery in most of Africa over the past 20 years has been mixed, with the critical element of equity almost consistently de-emphasized. For the majority of the poor, residing mainly in rural communities, household water security, including drinking water and sanitation, remains pathetic. Rural access to safe water and sanitary excreta disposal in sub-Saharan Africa was only 42% and 41%, respectively, at the end of 1999, although reaching 83% and 81% in urban areas. To promote a continent-wide water management principle of some for all and bring rural needs to the front of a coherent and sustainable resource development agenda, past policies are being revisited and reshaped, taking into cognizance country-level sector specifics and existing institutional capacities. The paper focuses on the situation in Swaziland, a small landlocked country in the southeastern corner of southern Africa. Based on an in-depth analysis of the thematic issues, concerns and constraints relating to the management of rural water supplies over the last two decades, from institutional, technical and environmental issues to social, financial and private sector partnerships, some key policy guidelines and implementation strategies are proposed. The proposals include the target of universal access to safe water supplies at the end of year 2020, building on the existing goals in national development plans.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".