Urban water security for developing countries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Populations in urban centers of developing countries have increased very significantly during the post‐1960 period, primarily due to urbanization. Rates of population growth during this period simply overwhelmed their financial, institutional, and technical capacities to manage all types of basic services, including the provision of clean water and proper wastewater management. Surprisingly, issues of access to clean water and sanitation at major international forums of very senior policymakers were first raised during the United Nations Conference, in Vancouver, in 1976. It recommended that everyone should have access to clean water by 1990. Subsequently, Millennium Development Goals set the target that, by 2015, the number of people not having access to clean water should be reduced by half, compared to 1990. The United Nations claimed that this target was met in 2010. However, this is not true. Thereafter, the Sustainable Development Goals stipulated that everyone should have access to clean water by 2030. Current developments indicate that this goal is highly unlikely to be reached. This paper objectively reviews the progress of urban water security in developing countries from the post‐1960 period, analyses why international targets were missed in the past, and what can be done to ensure urban water security in developing countries in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it