Microfinance institute’s non-financial services and women-empowerment: The role of vulnerability
Bibliographic record
Abstract
Women-Empowerment is one of the most crucial challenge in Pakistan. Pakistani women are contributing only 25-30% in nation's economy which is quite low as compared with other developed as well as developing countries such as United Kingdom (UK), United States of America (USA), Malaysia, China, Indonesia and India. To address this problem, the primary objective of this study was to examine the role of microfinance institutions in women-empowerment. Moreover, moderating role of vulnerability was also examined. Quantitative research approach and cross-sectional research design were adopted. Data were collected from the female clients of microfinance institutes in Southern Punjab, Pakistan. Survey was conducted to collect the data and questionnaires were distributed by using area cluster sampling. SmartPLS (SEM) was used to analyze the data. It was found that non-financial services of microfinance institutes such as training/skill development programs and social capital development had positive contributions towards women-empowerment. Moreover, vulnerability moderated the relationship between social capital and women empowerment. Thus, this study contributed in the body of literature by investigating vulnerability as moderating variable. Hence, this study is beneficial for microfinance institutes to enhance womenempowerment through training/skill development and social capital development.
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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 | 0.002 | 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 | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".