Seeking a “Supportive” Leader: Gendered language in leadership job postings and women’s leadership identification
Bibliographic record
Abstract
Evidence suggests that women are underrepresented in leadership positions in Canada (MacDougall et al., 2021). While past research has shown that masculine wording reduces job attractiveness for women, few scholars have examined the effect of gendered language in a leadership context. First, we conducted a directed content analysis to assess the prevalence of gendered language in existing Canadian leadership job postings. This revealed that gendered language exists in job postings at expected levels, with masculine language occurring more frequently, especially in male-dominated industries. Second, we explored how gendered language impacts women’s intentions to apply for leadership positions and the potential roles of stereotype threat and perceived self-efficacy. Results indicated that, for women with lower self-efficacy, feminine language in job advertisements leads to greater application intentions through its effect on leadership identification. These findings suggest that more inclusive, feminine language in leadership job advertisements may encourage more women to apply
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".