Role of Inequality and Inequity in the Occurrence and Consequences of Chronic Arsenicosis in India and Policy Implications
Bibliographic record
Abstract
Abstract More than 10 million people living in India face health risks from arsenic-contaminated groundwater. Arsenic originated naturally in the earth's crust in the Himalayan region and was deposited in aquifers for thousands of years. Arsenic exposure is taking place due to intensive use of groundwater in irrigation and household use (drinking and cooking). Although a better understanding of the problem has been arrived at, and a vast knowledge database is available, there has been no systematic effort to address the wider problem or to adopt truly equitable solutions. The author conducted the study in some arsenic-affected villages in West Bengal, the most severely affected state in India, and analyzed the existing research and policy documents in order to examine the extent of the suffering of the people and to explore the possible sustainable solutions appropriate in the local context. The study has established the role of socioeconomic disparities, governance, policy, and their complex relationships in the incidence, magnitude and consequences of chronic arsenicosis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".