Invisible or Clichéd: How Are Women Represented in Business Cases?
Bibliographic record
Abstract
Women represent just less than 50% of undergraduate business graduates and 36% of MBA graduates. Despite their strong presence in management education programs, women are noticeably absent from business case studies—a key pedagogical tool for instruction within management education programs worldwide. While case studies inform students about business processes, decision making, strategy, and leadership and management challenges, they also promote unintentional learning about gender. We argue that case studies contain a “hidden curriculum” that presents and reinforces implicit assumptions and stereotypes about women’s fitness to lead. Using NVivo 11 software to analyze the content of written cases, we examine the presence, absence, and representation of female and male protagonists in a sample of business cases published by a large business school case publisher. The findings offer comparative insights into the proportion of cases featuring female protagonists, the representation of women and men in leadership roles, and the characterizations of the female and male protagonists. Women protagonists were absent in more than 80% of cases, and when present, were portrayed as less visionary, risk taking, agentic, certain, and more emotional, cautious, and quality and detail oriented than men.
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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 | 0.010 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".