Social Capital and Basic Goods: The Cautionary Tale of Drinking Water in India
Bibliographic record
Abstract
This study uses micro-data from the 1998-99 Indian Time Use Survey (ITUS; covering 77,593 persons in 18,591 households in Gujarat, Tamil Nadu, Madhya Pradesh, Meghalaya, Orissa, and Haryana) to argue that time use data provides a natural metric for measuring "social capital" building activities and for distinguishing between the relative importance of "bonding" into groups or "bridging" within communities. The study examines the correlation between inequality in landownership, caste status, measures of local social capital, and whether or not a household will have to collect water. In India, the probability that a rural household fetches water is 4.8% and 9.1% lower in communities in which the average time spent on social interaction and community-based activities at the district-level doubles, but it is 18.9% greater when the time in group-based activities doubles. Inequalities in landownership and home ownership are associated with considerably larger differences in local tap water availability.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".