The experiences and challenges of black entrepreneurs in Halifax, Nova Scotia
Bibliographic record
Abstract
This study draws upon 20 semi-structured interviews with Black immigrant entrepreneurs (N=15) and Canadian-born Black entrepreneurs (N=5) in Halifax, NS to examine their lived experiences. Informed by the mixed embeddedness model, this study examines entrepreneurial motivations, \nbarriers and coping strategies across social, political-institutional and economic domains. It further \nadopts the intersectionality theoretical framework to examine how race, gender and migration \nstatus interact to affect the experience of these Black entrepreneurs. Findings reveal that majority \nof Black entrepreneurs are pulled into entrepreneurship because of good market opportunity, while \nalso reporting inadequate finance and racial discrimination as their most pressing challenges. The \nresearch has observed new knowledge about how Black entrepreneurs have developed a positive \nmindset and reliance on family and friends as their main response to overcoming challenges and maximizing opportunities. This study seeks to assist policy makers and practitioners to develop informed decisions that promote inclusion in the Canadian business environment.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.032 | 0.007 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".