Farmers’ Perception of Causes and Consequences of their Indebtedness in Haryana, 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
Abstract Subject and purpose of work: The study aims to highlight the perception of farmers regarding the causes and consequences of their indebtedness. Materials and methods: The study was based on primary data collected (by field survey) from a sample of 600 farmers. With regards to the selection of farmers or respondents, the proportionate sampling technique was employed. Percentage technique was used for data analysis. The data were collected in the first quarter of 2021. Results: It was found that 95.67% of the farmers (out of 600) reported low prices for agricultural output as being the main cause of their indebtedness, followed by crop failure (89.00%), the high cost of inputs (85.00%), high interest rates (61.17%) and small landholdings (58.83%). In addition, consequences reported by loanee farmers were deterioration in their social status (67.83%) and psychological stress (57.67%). However, positive changes experienced by farmers after repaying a loan were less than the negative experiences. Conclusions: The main causes of farmers’ indebtedness were crop failure and the high cost of inputs compared to the price of their produce. Due to their indebtedness, their economic and social status deteriorated and they experienced the feeling of insecurity.
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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 | 0.001 |
| 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