Characterizing social networks and their effects on income diversification in rural Kerala, India
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it