Farmers Characteristics and Its Influencing on Loans Resettlement Decision in Sri Lanka
Bibliographic record
Abstract
This study investigates the impact of socio-economic characteristics of the farmers and its impact on their loans resettlement behavior in the People’s Bank, Puttalam branch in Sri Lanka. Secondary data were collected from the bank officials and the data were analyzed with 100 applicants who are cultivating paddy as a major crop and other field crops during the Maha and Yala season 2011/2012. This study was analyzed using descriptive statistics, Tobit model and in addition to that elasticity of the loan repayment ratio also measured. The estimated Tobit regression model indicated that family members and secondary education were significantly positive influence on the farmers’ repayment behavior while loan disbursement has negative influence on their behavior in Sri Lanka. Other independent variables namely gender, age, civil status, major crops, income and higher level of education of the beneficiaries were not statistically significant influence on the farmers’ behavior in the above bank branch in Sri Lanka. The elasticity of the loan repayment performance for the variables also were calculated at the mean values and according to that number of family members, size of loans and secondary education were statistically significant. The overall results revealed that, the bank managers should considered the above characters of the borrowers to increase the probability of repayment ratio and thus it will help them to improve the efficiency of lending decision of the bank loans in future.
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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".