Damsels in distress: Discourses of entrepreneurship in management textbooks
Bibliographic record
Abstract
The purpose of this study is to provide insight into the discursive foundations of entrepreneurial identity for women in Canada by examining the social positions, representations and spaces they take in introductory business textbooks. We ask three questions: (1) What issues of power and discourses related to gender are subsumed in Canadian textbooks? (2) In what way is gender represented in texts of entrepreneurship in Canadian management education? (3) How are women portrayed in entrepreneurship texts? The data that we draw upon comes from 13 Canadian management textbooks that have been used in introductory business courses to teach students about the functions of business. By narrowing in on content in entrepreneurship education, we use poststructural feminism as a lens to interrogate gendered discourses that influence what and how students are taught about the ideals of entrepreneurship in undergraduate business programs in Canada. Our findings suggest that the entrepreneurship texts studied were overly gendered in masculine terms, serving to privilege the experience of male business while simultaneously marginalizing the representation of women entrepreneurs. We argue that marginalizing women in textbooks may form barriers to their interest and participation in entrepreneurial pursuits, and thereby call on scholars and practitioners to reconsider the importance of equality in materials used in the classroom.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.022 | 0.022 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".