Adversity and resilience-building in the Canadian entrepreneurial ecosystem: Using disaster, emergency management and social work to understand entrepreneurs' experiences
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Entrepreneurs—especially early entrepreneurs—face numerous challenges throughout their entrepreneurial journey. These challenges and adversities can create distinct personal and professional strains resulting in poor physical, mental, and emotional health. Thus, entrepreneurs must exercise resilience-building to properly prepare for, respond to, and recover from potential adversities. We frame adversities as “environmental shocks” to the entrepreneurial ecosystem using a disaster and emergency management and social work conceptual lens. Entrepreneurs subjected to these shocks then adopt resilience-building strategies as protective factors against future shocks, affording them the ability to bounce back or “bounce forward.” Using semi-structured interviews, we examined the types of adversities and resilience-building strategies employed by 27 Canadian entrepreneurs. Results indicated two forms of adversity and resilience-building—personal and professional— and the interplay within and between them. Personal and professional resilience included seeking therapy and financial preparedness while personal and professional adversity included isolation and problematic co‑leader relationships. Findings from the study call for entrepreneurial-specific social service and training programs which address the manifestations of adversity and offer practical strategies to enhance resilience. This research highlights a unique view of entrepreneurial adversity and resilience and offers a foundation for future research on Canadian entrepreneurial contexts.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it