Digital Technology as a Disentangling Force for Women Entrepreneurs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it