Bibliographic record
Abstract
Sanitation is an investment with high economic returns. Poor sanitation is a major public health issue in India. Poor sanitation is thought to be a major cause of enteric infections among young children. A third of the 2·5 billion people worldwide without access to improved sanitation live in India, as do two'thirds of the 1·1 billion practicing open defecation and a quarter of the 1·5 million who die annually from diarrhoeal diseases. Most of the rural population in India is unaware of the entrenched connection between cleanliness and health. The absence of sanitary conditions leads to many illnesses and diseases, which in turn lead to major social and economic problems of families and community as a whole. India’s sanitation deficit leads to losses worth roughly 6% of India’s gross domestic product and an estimated future losses equivalent to 3.4% of 2006 GDP. Hence the need of the hour is to undertake the initiatives to create awareness about sanitation and toilet culture in India like the recently launched “Swachh Bharat Mission” by the Government. The previous programmes and campaigns failed to bring about the change in the attitude and behavior of the people with regards to sanitation. The present campaign aims to involve all stakeholders to make it a people’s movement.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".