Impact of SHG’s on socio-economic development of Sugali Tribal women in Chittoor District, Andhra Pradesh
Bibliographic record
Abstract
Self Help Groups (SHGs) enable women to expand their savings and access the credit which banks are increasingly willing to lend. In the present study an attempt has been made to analyse the structure, conduct and performance of self help groups and their impact on socio economic development of 500 Sugali tribal women in Chittoor district of Andhra Pradesh, by administering a descriptive research design. 85 percent of the women in the study were married.71 percent of the sample comes under Nuclear family type, gained colony houses constructed by the Government and the formation of SHG was initiated by Government agencies to an extent of 80 percent. Illiteracy was noticed to an extent of 31 percent. In the study area a maximum of 93 percent of the SHG members use to have group meeting monthly once and also members use to save Rs. 50 per month. 72 percent of the sample was still surviving as members only and the same percent was observed in members lending amount from private sector banks while one quarter of them are lending from public sector commercial banks. 92 percent of the members expressed that they joined in the group to promote the culture of savings among themselves. Almost half of the sample was utilizing the loan amount towards their children education, cattle and construction of house. After joining the SHG, 98 percent of the women habituated for vivid purposes like independently to approaching the bank for their needs, speak with outsiders, communication skills to deal with the main stream life, awareness of rights and procedures and confidence to start a new SHG. In conclusion the results inferred that a remarkable change in their social and economic life was noticed among the Sugali tribal women upon becoming member in the SHG.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".