Constraints in credit accessibility from primary agricultural cooperative societies in Haryana state, India
Bibliographic record
Abstract
Abstract Subject and purpose of work: The objective of the study was to identify the constraints in credit accessibility from Primary Agricultural Cooperative Societies in Haryana State, India. Materials and methods: The primary data were used for achieving the objective of the study. The primary data were collected in the second quarter of 2022 by employing the schedule. The multistage purposive cum proportionate random sampling techniques were adopted to select a sample of 540 respondents. The percentage was used for data analysis. Results: The constraints in accessing credit are identified faced by beneficiary farmers and nonbeneficiary farmers. The major constraints faced by the beneficiary farmers in accessing credit from PACS are inadequate credit limit (87.40%), short time to repayment of loan (66.29%), high penalty rate (55.92%), and unavailability of medium-term loans (50.00%). While inadequate credit limit (80.00%), no new accounts opened (72.22%) and fear of being a defaulter (47.78%) were the major constraints faced by the non-beneficiary farmers in accessing credit from PACS. Conclusions: The various constraints were identified in accessing credit from PACS. Credit limits should be extended on the basis of the scale of the finance. Medium-term loans need to be increased.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".