The Double Jeopardy of Ethnic Minority Women Entrepreneurs in Canada
Bibliographic record
Abstract
Women entrepreneurs are under-researched in the entrepreneurship literature, in general, and in the ethnic-based literature, in particular. A 1982 to1995 review found only .007% of articles (22 out of 3206) focused on women entrepreneurs (Baker et al., 1997). A second 1995 to 1997/98 review, found .06% of articles (28 out of 435), across six journals and one proceedings, focused to some extent on women entrepreneurs (Brush & Edelman, 2000). An ethnic entrepreneurship specific, 1988 to 1999 literature review, across a range of academic discipline, found that only .04% (3 out of 80) of articles were focused on gender (Menzies, Brenner, Filion, in press). Various arguments are proposed for the lack of entrepreneurship gender-specific studies (Brush & Edelman, 2000). These arguments centre on the theme that more similarities than differences have been found in past studies, hence the redundancy of further study. There is also a bias towards studying large organizations, where male entrepreneurs predominate. Furthermore, gender is not in the forefront in terms of the currently popular research themes. Notwithstanding the very limited number of research studies on women entrepreneurs, there are many derogatory “myths” relating to women entrepreneurs. The US Diana Project (Brush, Carter, Gatewood, Greene, Hart, 2001) examined eight myths relating to women entrepreneurs. These myths included, for example, women don’t want to own high growth businesses, they don’t have the right education or experience to lead high growth businesses, they lack suitable networks, they are not financially savvy, and they don’t submit business plans. Women entrepreneurs who belong to an ethnic minority group are subject to a double jeopardy. They are disadvantaged because they are women and they are doubly disadvantaged if they also belong to an ethnic minority group. In this paper, we explore this double jeopardy, by examining the “derogatory myths” proposed in the US Diana Project. Data used in this study is based on a study of
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".