Women Entrepreneurs in the Middle East
Bibliographic record
Abstract
Straddling North Africa and Western Asia, the Middle East has been a cradle of civilisation and entrepreneurship - well before the arrival of Islam. In this region, gender roles were traditionally specified by culture, with women often expected to stay within the family environment, while men would trade in society at large. This book contributes to the literature on a highly neglected field of study: women entrepreneurs in the Middle East. Recognising that entrepreneurship does not take place in a vacuum, it focuses on contexts, and the ecosystems of this region with largely patriarchal societies, that are influenced by culture, religion, and colonial experience. This book provides readers with a topical analysis of women entrepreneurs in the Middle East on the context, ecosystems, and future perspectives for the region. Authors have presented the reality of 11 countries from the region based on women entrepreneurs' historical backgrounds, challenges, and achievements, as well as the contribution towards economic development in their local/immediate communities and the Middle East at large. Following the country analysis by the authors of each chapter, the editors provide a general assessment of the future of women entrepreneurs in the region by focusing on the current entrepreneurship policy and strategies of various countries in the region. This volume will be an essential reading for anyone researching or working on projects related to women's entrepreneurship and small businesses in the Middle East.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.004 |
| 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".