Bibliographic record
Abstract
Capacity building through training is now considered a vital component in maintaining competitiveness in the agricultural sector of less developed country like Pakistan.The study is conducted on beneficiary farmers at grass root level interested in getting loans from banks for crop production techniques and financial management inter alia.This was aimed at improving the loan repayment performance of the project beneficiary farmers.The sub-urban area of district Lahore and Kasur has been used as a pilot study to mainly assess the effects of training on loan repayment among the beneficiary small scale farmers regarding Agricultural and Micro Financing facilities from Banks & Specialized Financial Institutions.Questionnaires were administered to collect data from 150 respondents, sampled using the Judgmental or Purposive Sampling Technique.The data collected were analyzed using the SPSS-20 software and the descriptive statistical tools of frequency, regression and correlation for relationships of variables have been applied.The results showed that farmers obtained higher crop yields resulting from the application of the crop production methods trained in the SBP also needs to improve upon its frequency and timing of monitoring and recovery of the funds disbursed under the project.The SBP should also consider including in its training regime effective marketing strategies to enable the farmers sell their produce and pay off the loan in time to minimize Non-Performing Loans.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.976 | 0.978 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".