Self-Regulatory and Self-Governance Frameworks of Microfinance Programme in Kerala
Bibliographic record
Abstract
We assess the constitution, mode of operation, and self-governance and self-regulatory frameworks of Self-Help Groups (SHGs) Federation in a microfinance programme in a Coastal region in Kerala.This article uses the individual data of 100 microfinance participating households and the data of 15 credit delivery groups in the Evantualic Social Action Forum (ESAF)-MFIL/ESAF-SHG federation in Kerala, India during 2011 and 2019.The data collected from the Trikkunnapuzha, a coastal region of Kerala and the family members of SHGs are mainly Fishermen.This study shows that formation of sample SHGs have been carried out by the field staff (credit officers) of ESAF-MFIL, by utilizing local information networks to mobilize members of new SHGs.This study also shows that SHGs operate with the help of a hierarchy of organizations, namely, branches of ESAF-MFIL and tiers of ESAF-SHG federation.We find that the presence of SHG members in the weekly meetings of SHGs and the participation of members in the discussions at the meetings are crucial for ensuring the smooth functioning of SHGs.We also find that members' compliance with the 'rules' and 'norms' of SHGs, and various other governance attributes are important for the smooth operation of SHGs and delivery of financial services by an MFI.We also find that various governance attributes, such as attendance of respondents, maintenance of transparency, awareness of members about the bye-laws of the group, equal treatment of SHG members, verification of accounts, maintenance of accounting standards of SHGs, responsiveness of SHGs and consensus among the members, are interdependent and crucial in the operation of sample SHGs.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".