An overview of reasons and ramification for farmer suicides in India
Bibliographic record
Abstract
Agriculture is the back bone of Indian economy. It feeds 125 crore human population of India directly or through the generation of economic activities in the country, which is re instated in the resilience of Indian economy even in recent difficult days of global recession. In the 18 years period between 1996 to 2013 as many as 2,82,914 farmers committed suicide in India (1996 to 2013). Farmer suicides have decreased at annual compound growth rate of around 0.05 per cent per annum (1996 to 2013). The general suicides have increased at annual compound growth rate of around 2.19 per cent per annum. The total plan outlay towards agriculture and allied sectors has increased from I FYP (Rs. 354 crores) to II FYP (Rs. 50,924 crores) but the percentage of such an allocation to the total outlay has been decreased from nearly 14.90 per cent to 2.40 per cent over the years. A large number of Indian farmers are under debt trap due to variety of reasons. A major one among them, is the crop failure, leading to non-repayment of loans taken to raise that crop, thus, unable to get institutional credit for the succeeding seasons. The subsidy given towards agriculture has increased from Rs. 45,529 crores to Rs. 66,989 crores, but compared to other countries like Canada, Japan, USA the subsidy given to Indian farmers is far less. The gap between MSP and cost of cultivation in majority of the crops was minimum of 32 per cent except, Sugarcane, where the gap was the least (12 %). Effective collaborations, co-ordination, co-operations, commitment to the cause of the farmers' upliftment are needed to prevent farmers' suicide.
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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 | 0.001 | 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 | 0.000 | 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".