Two Sets of Business Cards: Responses of Chinese Immigrant Women Entrepreneurs in Canada and Australia to Sexism and Racism
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Existing entrepreneurial discourses have been dominated by white middle-class androcentric approach, giving little space to the discussions of racism and sexism experienced by minority women entrepreneurs. This paper aims to fill this gap through an examination of the experiences of Asian immigrant women entrepreneurs in Canada and Australia using an intersectional approach. The key research question addressed in the paper is to what extent, and in what ways, do racism and sexism impact on the entrepreneurial experiences of Asian immigrant women entrepreneurs and what strategies do they use in managing discrimination to protect themselves and their businesses? Four main strategies were derived from our findings, namely, creating a comfortable niche, playing the mainstream card, swallowing the pain, and resisting.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it