Assessing Admiration for Women Who Do “Men’s Work”
Bibliographic record
Abstract
Abstract Drawing on interviews and focus groups from Conakry, the capital city of the Republic of Guinea in West Africa, this article examines how people talk about women working in male-dominated skilled trades alongside women’s accounts of their work experiences in those sectors. We find that the idea of women doing gender atypical work, whom we call “crossovers,” evokes widespread admiration. They are unanimously described as brave and virtuous, contrasted with women who rely on money from relationships with men. However, this celebration falters in the workplace, where crossovers often experience paternalism and harassment. Building on theories of both gender beliefs and femininities, we attribute this discrepancy to the differential threats to the gender order that are posed by accommodating crossovers at work versus speaking positively about them. Working together requires men to confront actual women’s unexpected capabilities, while rhetorically celebrating crossovers may in fact reify stereotypes about most women and fail to fundamentally undermine men’s authority. Crossovers can serve as sources of inspiration for an alternative gender order, but we find that professed admiration for “exceptional” groups of women has both limitations and risks. We conclude by suggesting that the subversive potential of admiration for gender atypical behavior must be empirically examined, rather than assumed, with attention to why such women are seen as admirable as well as how this admiration is borne out in social interactions.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| 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".