The development of a low-cost sanitation system suitable for Botswana
Bibliographic record
Abstract
Recent studies have revealed that Botswana has probably the highest urban growth rate in Africa and although its four towns are relatively small, their annual population increase in percentage terms is extraordinary. It is estimated, for example, that Gaborone the capital could well grow from its present 33,000 to more than 130,000 by 1985; a fourfold increase within the next decade. The majority of these extra people will be low-income families, all of whom will need homes and the Government is alive to the need to secure their settlement in a planned manner, if we are to avoid a serious squatter situation, in site and service housing areas. The key to successful urban growth in the context of a developing country is a system of domestic sanitation which is inexpensive, efficient in operation, hygienic and socially acceptable and it is for this reason that the Government has authorised a wide-reaching research study.into low-cost sanitation to be carried out by my Ministry in conjunction with the Ministries of Health and Works and Communications and with the assistance of the International Development Research Centre of Canada. I welcome this project and hope that the study and its findings will be of lasting benefit to Botswana and to other countries with similar problems. Foreword by the Hon. L. Makgekgenene Minister of Local Government & Lands Republic of Botswana
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".