Adversity and resilience-building in the Canadian entrepreneurial ecosystem: Using disaster, emergency management and social work to understand entrepreneurs' experiences
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.022 | 0.011 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".