Entrepreneurship as a driver of rural women’s empowerment in Iran
Bibliographic record
Abstract
Entrepreneurship is a key driver of empowerment, particularly in rural areas, as it creates opportunities for self-sufficiency and sustainable development. It enables individuals to overcome barriers, build capacity, and engage in participatory activities that foster economic and social growth. This study aimed to analyze entrepreneurship and the empowerment of rural women, as well as the factors influencing their empowerment. A quantitative approach was employed using a survey method in Fars Province, Iran. Stratified random sampling was applied, and the sample consisted of 393 rural women. Data were collected through questionnaires, with face validity and reliability confirmed by university professors, and a pilot study conducted prior to the main survey. Data analysis was performed using SPSS25, SmartPLS3, and AMOS21 software. The results indicated that both entrepreneurial spirit and entrepreneurial activities significantly influenced the empowerment of women entrepreneurs. Additionally, factors such as entrepreneurial motivation, social capital, entrepreneurial knowledge, and social networks were found to significantly impact rural women's empowerment. Entrepreneurial women in Marvdasht County serve as role models for other rural women due to their strong entrepreneurial motivation and business success. The study recommends offering educational programs focused on cognitive factors and enhancing social networks as essential strategies to empower entrepreneurial activities among rural women.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".