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.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".