Digital Technology as a Disentangling Force for Women Entrepreneurs
Bibliographic record
Abstract
This study investigates the empowering potential of digital technologies for women entrepreneurs, a transformative force that transcends all fields of knowledge. It specifically examines how technology can equip women to overcome socio-cultural and economic barriers, focusing on the case of Iran. The research employs a mixed-methods approach, utilizing a literature review within the qualitative framework to identify key empowerment drivers. Subsequently, a quantitative approach leverages DEMATEL to pinpoint the most impactful drivers. This investigation aims to provide stakeholders with actionable insights, highlighting the critical role of technology in fostering equitable and sustainable economic advancement for women entrepreneurs. Furthermore, the study emphasizes the importance of gathering information from a developing nation like Iran, as its findings can hold significant implications for other countries experiencing similar developmental stages. Ultimately, the research seeks to inform the creation of effective policies, support initiatives, and educational programs. These interventions aim to empower women entrepreneurs to leverage digital tools for sustainable business growth, ultimately contributing to a more equitable and environmentally conscious future.
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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 | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".