Farmers’ Perception of Causes and Consequences of their Indebtedness in Haryana, India
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.
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.
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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".