Coping with Indeterminate Dangers in a Violent Conflict Zone: A Study of Libyan Women Entrepreneurs
Bibliographic record
Abstract
Responding to the call for recalibrated entrepreneurship research that embraces the pluralism of entrepreneurial activity and is open to contextually grounded theorizing, this paper investigates the lived experiences of women entrepreneurs in a violent conflict zone. Although such contexts may seem ‘extraordinary’ relative to the stable and benign settings underlying the vast majority of extant scholarship, unfortunately, such environments are ‘ordinary’ for the millions of people who live within them. Adopting an inductive theory-building approach, we analyzed qualitative data collected from interviews with 30 women who had launched businesses in Libya—a country that has become one of the most dangerous in the world after the 2011 Arab Spring uprising. Our findings illuminate the indeterminate everyday dangers that exist for women entrepreneurs in such a context, the tactics that they implement as attempted coping/surviving mechanisms, and the consequences for themselves, their businesses, and other citizens. Collectively, the emergent insights from our study not only extend—but also challenge—several taken-for-granted understandings about entrepreneurship, derived primarily from Western theory and research, in which entrepreneurs are presumed to operate in environments of relative peace.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".