Empowering Self-Help Groups: The Impact of Financial Inclusion on Social Well-Being
Bibliographic record
Abstract
Financial inclusion (FI) relates to the access and availability of financial services to society, especially in low-income groups. FI is pivotal in achieving 7 of the 17 Sustainable Development Goals (SDGs). This paper explores the level of FI in the rural areas of Maharashtra and measures the impact of FI on the social conditions of Self-Help Groups (SHGs) prevalent in these areas. The study is based on a 424 SHGs survey conducted in the Pune, Thane, and Palghar districts of Maharashtra, India. The impact of FI on SHGs is evaluated using a Structural Equation Model (SEM). The results of the study show that physical banking services, Business Facilitators (BFs), and Business Correspondents (BCs) improve the social conditions of rural SHGs. Additionally, BCs and BFs mediate the relationship between physical banking services and social conditions. The study also reveals an insignificant association between BCs and BFs and insurance services. The present study highlights the importance of increasing the awareness of insurance policies through financial literacy programs and making timely availability and accessibility of BCs and BFs to enhance financial inclusion in rural areas.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".