Negotiating farm femininity in agricultural leadership
Bibliographic record
Abstract
A growing number of women in the Canadian Prairie region are advancing into leadership roles in agriculture, which remains a predominantly male domain. In this research we explore how professionally and managerially employed women in agriculture in the provinces of Manitoba, Saskatchewan, and Alberta navigate being a leader in an industry characterized by rural hegemonic masculinity. We explore and examine the personal experiences and observations of these women regarding gender, leadership, and the current state of prairie agriculture as it grapples with being more inclusive, diverse, and equitable. We found that to gain legitimacy as a leader in agriculture women are enacting a complex mix of traditional femininity, anti-affirmative action, and masculine-coded farm credibility. Women are required to be both like a man and like a woman to differentiate themselves—both from men and from one another—as they navigate both similarity and difference in their gender performance. Expanding on the work of Mavin and Grandy’s (2016) work on respectable business femininity, we have conceptualized this performance as “respectable farm femininity” to reflect the specific experiences, and previously unexplored domain of women in agricultural leadership (outside of the on-farm contexts that make up the scholarship in this area). These expectations are rooted in more traditional constructions of rural, hegemonic masculinity, but carry important weight in conferring legitimacy to women in agricultural leadership. This has important implications for how women are able to carve out their career path on the way to leadership.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.020 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".