Reforming Cooperative Credit Structure in India for Financial Inclusion
Bibliographic record
Abstract
CHAPTER 1deposits and credit, and most of them have been earning profits.The third and the lowest tier in the short term credit system at the rural level is the Primary Agriculture Credit Societies (PACS).The examination of data of PACS at a national level reveals that the total membership for 2011-2012 was 127.4 million (NAFSCOB, 2012) and the number of total borrowers was 45.2 million individuals, and total deposit 189760 million rupees for 2012.Corresponding figures for some of the previous years, for example, for 2004-2005 are: total membership of PACS 135.4 million individuals; number of borrowing members 51.3 million, and total deposit 181430 million rupees.Although the total numbers of borrowers had fallen, interestingly the total deposit had increased in the same period.The data revealed that southern states had the highest number of members per PACS at 3256 (NAFSCOB 2012).The western states had the lowest number of members at 577 persons per PACS.Further, the low number of members per PACS in the western states could be because of the relatively higher number of PACS located in these states.The average number of borrowers per PACS is 507 across India, with the highest number of borrowers being in the southern region with 1514 persons and the lowest number of borrowers from the northeastern region with 61 persons (See table 1.1).Table 1.1 Distributions of PACS by Region, 2011-12 Regions Total Number of PACS
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".