Women Entrepreneurship Development in Yemen: The Role of Decision-Making Empowerment
Bibliographic record
Abstract
This research was conducted to examine the impact of decision-making empowerment on women entrepreneurship development in Yemen. Two dimensions of decision-making empowerment were used; economic decision making, and household decision-making empowerment. This study employed the quantitative approach and the method of collecting data was the online-questionnaire. The targeted sample size of this study was 200 business women in Yemen and the response rate was 96.5%. The results of the correlational analysis show that there is a clear strong positive correlation between decision-making empowerment and women entrepreneurship development. In addition, the two dimensions; economic decision making, and household decision-making have a significant relationship with women entrepreneurship development. The results of the regression analysis reveal that decision-making empowerment has a significant impact on women entrepreneurship development and the economic decision-making empowerment and household decision-making empowerment were explaining women entrepreneurship development. It can be concluded that empowering women by allowing them to participate in economic and household decision-making appears to be one of the important factors for developing women's entrepreneurship in particular, which in turn will help reduce poverty as well as achieve economic growth.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".