Social and Economic Consequences of Agriculture Crises: A Study of Farm Labour in Punjab, India
Bibliographic record
Abstract
This study aims to comprehensively analyze the multifaceted socioeconomic challenges faced by agricultural laborers in Punjab, India, stemming from the capitalist agrarian practices introduced during the era of the Green Revolution. It delves into employment patterns, debt burdens, and household conditions to uncover the complex realities these laborers endure. Additionally, the study seeks to fill a significant research gap, as most economists emphasize the problems faced by land-owning farmers, often overlooking the substantial issues confronting agricultural laborers who constitute a large share of the total working population. Utilizing a mixed-method approach, this research combines primary data from a comprehensive multidimensional survey with a critical review of secondary literature. The findings from this approach reveal the profound socioeconomic vulnerabilities faced by these laborers. The majority is ensnared in severe debt, grapple with unemployment, and endure substandard living conditions, with many lacking access to basic necessities such as decent housing and sanitation facilities. Due to their limited access to institutional credit facilities, agricultural laborers are forced to seek credit from non-institutional sources at exorbitant interest rates. Shifting cropping patterns in favor of wheat-paddy crop rotation, seasonality of labor, and labor-saving techniques such as extensive mechanization of agriculture and the use of herbicides have resulted in shrinking employment opportunities, further aggravating their economic plight. In response, the study proposes policy recommendations including radical land reforms, strengthening the public distribution system, providing affordable loans, ensuring employment opportunities, and enhancing social welfare measures. Implementing these recommendations is crucial to addressing systemic issues and improving the socioeconomic conditions of agricultural laborers in Punjab.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".