To Confront or Not to Confront? Effective Male Allyship in Entrepreneurship
Bibliographic record
Abstract
Allyship, where advantaged individuals support those from disadvantaged groups, plays a critical role in fostering inclusion. Considering that males typically form the advantaged group in entrepreneurial pursuits, we conduct two studies to advance academic understanding of whether and how allyship can help counter sexism and foster equity for female entrepreneurs. Building on qualitative interviews with 35 female entrepreneurs, Study 1 reveals four categories of effective allyship: boosting self-belief, encouraging entrepreneurial engagement, showing gender sensitivity, and removing barriers through confrontation. Interestingly, confrontational allyship emerged as impactful yet polarizing: moreover, its potential benefits seem to depend on the way how it is enacted. Building on these preliminary findings, Study 2 used an experimental design with 441 participants to test how female entrepreneurs value different types of confrontational allyship behavior (collaborative vs. advocative vs. no help). Observing that women prefer both collaborative and advocative help over no help when facing sexism, our work introduces the transformative potential of male allyship in entrepreneurship and highlights different ways in which men’s involvement as allies can contribute.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".