Personas: Understanding Black Entrepreneurship in Canada | Personas: Comprendre l’entrepreneuriat Noir au Canada
Bibliographic record
Abstract
<b>Executive Summary</b> <p>The Black Entrepreneurship Knowledge Hub (BEKH) is part of the Black Entrepreneurship Program, aimed at understanding the unique experiences of Black entrepreneurs across Canada. The National Qualitative Study, led by the University of Alberta, uses persona-based research to highlight the needs and challenges of Black business owners. Findings from the study will support policymakers in designing more effective programs and policies that promote the growth and success of Black entrepreneurs.</p> <p>The study created personas to represent the diverse experiences of Black entrepreneurs. Focus groups were conducted across six regional hubs, engaging 52 participants. A symposium will be held in 2025 to validate findings and collect feedback.</p> <b>Résumé Exécutif</b> <p>Le Carrefour du savoir pour l'entrepreneuriat des communautés noires (CSEN) fait partie du Programme d’entrepreneuriat des communautés Noirs (PECN). Il vise à mieux comprendre les expériences uniques des entrepreneurs noirs au Canada. L’étude qualitative nationale, dirigée par l’Université de l’Alberta utilise une approche fondée sur l'utilisation des personas pour mettre en lumière les besoins et défis des propriétaires d’entreprises noires. Les résultats de l’étude aideront les décideurs politique à concevoir des programmes et politiques plus efficaces favorisant la croissance et le succès des entrepreneurs noirs.</p> <p>L’étude a créé des personas pour représenter les diverses expériences des entrepreneurs noirs. Des groupes de discussion ont été menés dans six moyeau régionaux, réunissant 52 participants. Un symposium aura lieu en 2025 pour valider les résultats et recueillir des commentaires. </p>
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.037 | 0.010 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".