Impact of refugee women's entrepreneurship on socioeconomic outcomes and well-being: A scoping review
Bibliographic record
Abstract
Purpose: The aim is to contribute to a broader and more nuanced understanding of the experiences and challenges these women face as entrepreneurs, to aid in the development of more effective supportive measures. Method: A scoping review methodology guided by the framework proposed by Arksey and O'Malley and later refined by Levac et al., and PRISMA ScR reporting guidelines was conducted. We identified and synthesized themes from twelve pertinent articles that met our review criteria. Findings: Four overarching themes were identified that included (1) the contextual influences on entrepreneurship, (2) the constraints and barriers faced by refugee women entrepreneurs, (3) the resilience and resourcefulness displayed by these women, and (4) the outcomes of their entrepreneurship. These themes reveal the complex interplay of factors that shape the social, economic, and health impacts of entrepreneurship on refugee women. Discussion and Conclusion: This study underscores the urgent need for more rigorous scholarly attention to the area of refugee women’s entrepreneurship. While themes identified in this study align with previous literature, they have not been thoroughly addressed within a consolidated research framework in existing peer-reviewed studies. Therefore, our review is instrumental in augmenting the existing knowledge base and illuminating new directions for future scholarly investigation.
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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 | 0.014 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".