Characterizing social networks and their effects on income diversification in rural Kerala, India
Bibliographic record
Abstract
Income diversification continues to be a key strategy for poor rural households, including those that are progressively developing and those operating under increasing distress. The ability of a household to diversify has been shown to depend upon its demographic and economic characteristics and its physical and social context. This paper considers the effects of intra-village social networks on household income diversification in one of the poorest and most ethnically diverse areas of the Indian state of Kerala. Using techniques adapted from spatial econometrics, we find that social connections within a village magnify the impacts of household characteristics such as education and number of adults by a factor of 3.6 times. Models with alternative measures of network centrality (degree and eigenvector) indicate that the number of network connections that a household has is more important than the centrality of those connections. Finally, we use social contact information to calculate assortative mixing based on caste. The results suggest social stratification in these villages, with higher levels of stratification associated with lower levels of income diversification.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".