Access to Non-farm Employment in Contemporary India: A Study of Bihar and Punjab
Bibliographic record
Abstract
Rural non-farm diversification in India is taking up new roles amidst increasing agrarian distress. In this context, two issues have been examined in this paper: first, the nature of rural non-farm diversification, and second, the accessibility of households to rural non-farm employment in the states of Bihar and Punjab. The study is predominantly based on unit level data of the latest round of the Situational Assessment Survey of Agricultural Households (NSSO). Findings suggest that while non-farm activities are largely adopted by landless and marginal land households in both states, there are a few lucrative options available which are being accessed by large landholders in Punjab. Overall, caste, gender and education are dominant determinants that work as barriers to the entry for rural households. The findings recommend that institutional reforms along with public policies should be prioritised towards generating sustainable non-farm livelihood options while eliminating multi-dimensional exclusions in rural labour markets considering regional prerequisites.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".