The Role of Socio-economical Factors of Micro-credit Funds in Improving Rural Women Entrepreneurship Development
Bibliographic record
Abstract
This study tries to identify the individual, social and economical role of rural women’s micro-credit funds of Semnan city, in developing the entrepreneurship of women. To achieve this goal, 170 women of four funds of Semnan villages were selected. Independent variable factors including economical, social and cultural factors via funds in to dependent variable that is women’s entrepreneurship developing and itself classifies to and entrepreneurs characteristics (risk, internal, control focus, opportunism, ambiguity tolerance, innovation) were studied. The study uses the correlation methodology and the type of selection is sampling (N=170). The questionnaire was used as a tool of gathering information. For a descriptive evaluation, the questionnaire has been answered by supervisors, experts and consultants in agricultural extension and education field who are responsible for Semnan credit funds. For the reliability of the results, 300 questionnaires were filled out by 30 female members of the funds other than Semnan town funds. (Om abeha) rural women funds of Darjazin. Filled out questionnaires were calculated by SPSS software and kronbakh alpha coefficient. krnbakh alpha of 88.5% show the extend of which different parts of the question are reliable and validity. The results of multi – regression show that (satisfaction of presented activities and self confidence) that are cultural- social factors of fund, and (scale of income from plan and theory than marketing and market survey and sale) that are economical factors of fund, have important and basic role in women entrepreneurship developing.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".