Examining Post-Traumatic Growth and Resilience Through Entrepreneuring
Bibliographic record
Abstract
Entrepreneurship has a long history of being the economic outlet for populations that do not “fit” in the traditional workforce: refugees, veterans, survivors of domestic or generational violence or abuse; as well as for individuals coping with other extreme forms of adversity. A common thread connecting these populations is their exposure and experiences with trauma. Our focus in this symposium is to consider entrepreneurship as more than economic support or a mechanism solely for wealth creation. Instead, we explore the topic of when and how does entrepreneurship foster post-traumatic growth and resilience. Post-traumatic growth is an established positive response to trauma exposure, and the papers in the symposium explore how civilians in a war-torn country, indigenous populations living in economic disadvantage, and women refugees who survived the same traumatic event all achieve such an outcome. Collectively, these papers explore how entrepreneurs use different mechanisms associated with creating, building, or running a new business to advance in healing and positive adaptation to adverse events. Data suggests that trauma exposure impacts at least 70 percent of the general population, therefore the insights from these papers illustrate the healing potential of entrepreneurship across all communities. Water, Water Everywhere, But Too Many Drops to Drink? Social Opportunity Prioritization Author: Nataliia Yakushko; The University of Tennessee-Knoxville Indigenous Entrepreneurs’ Everyday Work of Coping with Intergenerational Trauma Author: Katrin M. Smolka; Author: Ali E. Ahmed; Author: Deniz Ucbasaran; Re-discovering Human Care and Trust: The Role of Mentors in Mitigating Wellbeing Risks Post-trauma Author: Michelle Richey; Loughborough University Entrepreneurial Rebellion: How Women Entrepreneurs React in Context of Persistent Acute Adversity Author: Mona Itani; American University of Beirut Author: Ramzi Fathallah; University of Ottawa Author: Rayan Fawaz; University of Sussex Author: Shintaro Okazaki; King's College London Author: Dima Jamali; Entrepreneurship as a Mechanism for Post-Traumatic Growth Author: Arielle M. Newman; Syracuse University Author: Elizabeth Embry; University of Kansas
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".