Bibliographic record
Abstract
Arsenic contamination of groundwater is one of the most serious environmental health disasters occurred in India. Out of an estimated 9.7 million arsenic exposed population, nearly one million are showing various forms of clinical manifestations, including cancers. The current study aims to assess the social and health vulnerability of the affected population to cope with arsenic pollution at the household and community levels. In arsenic affected villages, an extensive study has been conducted, which included household surveys, focus groups, informal discussions and interviews of concerned authorities, civil society organizations, political leaders and technical experts. Secondary data was collected by reviewing literature and policy documents. The World Health Organization’s (WHO) International Classification of Impairment, Disability and Handicap (ICIDH) was used to measure the consequences of arsenicosis. Poverty was one of the major determinants of arsenic exposure level, severity of manifestations and consequences, which has a link with a subject’s occupation, nutritional status, access to health care and good governance. The existing knowledge gap between the scientific community and local government has been the major obstacle in implementing a sustainable mitigation strategy. Social disparity (including gender) and lack of a political will have resulted in poor community participation during decision making and grass root planning respectively. Hence, several strategies cannot benefit in terms of improvement of symptoms. Rather, physical disability and disfigurement due to symptoms have made the poor more vulnerable to economic and social exclusion. The study has revealed that there is a need to incorporate the social determinants of arsenicosis in mitigation policy in order to reach out to the vulnerable section of the community.
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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".