Exploring entrepreneurial challenges and drivers in small cities for poverty reduction by investigating insights of key stakeholders: A case study of Saint John, New Brunswick
Bibliographic record
Abstract
The potential of entrepreneurship to reduce poverty is closely tied to critical factors such as access to finance, training and education, networks and social capital, and supportive regulatory environments. Understanding and addressing these underlying issues through the lens of the Social Capital theory can help foster an entrepreneurial spirit in cities and mitigate poverty through business and community development. This paper explores the insights and standpoints of key stakeholders about poverty in Saint John and its impact on entrepreneurship. The study uses a quantitative method and analyzes data from surveys with stakeholders. The results show that social isolation, system inflexibility, individual issues, housing, and financial support programs are significant poverty challenges in Saint John, and these issues have implications for entrepreneurship. By integrating Social Capital Theory into policy initiatives, policymakers can enhance community resilience and empower vulnerable individuals. This application of social capital principles provides a holistic framework for designing effective poverty-reduction measures, offering transformative insights applicable not only to Saint John but also to diverse small cities. The study contributes a nuanced understanding of poverty’s impact on entrepreneurship, advocating for inclusive strategies that resonate with the social fabric of communities.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".