Micro Credit Performance of Banks under Swarnajayanti Gram Swarojgar Yojana in Dibrugarh District of Assam: An Econometric Analysis
Bibliographic record
Abstract
The Swarnajayanti Gram Swarojgar Yojana (SGSY) scheme was introduced with the aim to alleviate poverty with a view to assisting the poor to bring them above the poverty line. The study is conducted to analyze seasonal variations in the quantitative time series data in micro credit performance of banks under SGSY scheme in the Dibrugarh district of Assam. The study uses the secondary sources of data. Statistical tools for the present study include compound annual growth rate, trend analysis, Pearson correlation coefficients and ANOVA for regression. It is inferred from the testing of hypothesis that banks operating in Dibrugarh district did not grant credit according to credit target fixed for the period 08-09 to 12-13. It is observed from the study that there is much variation in the growth of number of SHGs formed and amount of revolving fund received, revolving fund released, loan sanctioned, subsidy released and loan /Subsidy disbursement during third quarter 08-09 and second quarter 12-13 due to human forces. The econometric analysis of seasonal variation in micro credit performance of banks under SGSY reveals that that the majority of the F scores for the regression coefficients for each model are found to be statistically significant which in turn implies the overall significance of the model concerned. The cubic model is considered on the ground that the R square value of cubic model is greater than the other forms of models. Thus, cubic model fits the micro credit performance of banks in the time series data.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".