Trauma & Entrepreneurship: Challenges and Opportunities Across Three Contexts
Bibliographic record
Abstract
Trauma, whether experienced individually or collectively, can profoundly influence entrepreneurial motivation, behavior, and well-being. Through three interrelated papers, this dissertation examines how trauma can simultaneously constrain and catalyze entrepreneurial action. The first paper investigates how a large public research university responded to the cascading collective traumas of the COVID-19 pandemic by embracing intrapreneurial thinking. By launching a virtual incubator to support marginalized entrepreneurs worldwide, the institution not only addressed societal needs but also bolstered its adaptability, legitimacy, and social impact. The second paper introduces a trauma-informed framework for refugee entrepreneurship support, grounded in principles of trauma-informed care to address traumas often experienced by refugees. By centering psychological well-being and trauma-informed interventions, this framework offers practical guidance for tailoring entrepreneurship programs to the unique needs of refugee entrepreneurs. The third paper employs Freire’s critical consciousness theory to explore the entrepreneurial endeavors of bar hostesses (i.e., sex workers) in Nairobi, Kenya. We observe that despite confronting overlapping traumas – stigmatization, COVID-19 disruptions, and systemic marginalization – they exercise entrepreneurial autonomy and collective emancipation. Their experiences underscore how dialogue, critical reflection and action, and empowering entrepreneurship education can spur resilience, community-building, and social change. Taken together, these three studies highlight trauma’s dual capacity to hinder and inspire innovation and social impact. By illuminating social intrapreneurship, trauma-informed care, and critical consciousness, this dissertation provides new insights into how individuals and communities facing profound adversity and trauma can leverage entrepreneurship for transformative healing, empowerment, and inclusive economic development.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.032 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.001 | 0.022 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".