No Barriers for Trailblazers? Empowerment Messaging Increases Women’s Burden and Blame for Gender Inequality in the Canadian Armed Forces
Bibliographic record
Abstract
Gender inequality persists in the workplace, including in the Canadian Armed Forces (CAF). The CAF is struggling to attain their goal of increasing the representation of women in uniform from 16% to 25%. One factor that may be contributing to their struggle is the cultural response of empowerment messaging to gender inequality. Empowerment messaging coveys that women can succeed in life through personal agency and optimism about the future. However, this seemingly positive messaging has the negative effect of increasing attributions of women’s responsibility for gender inequality while disregarding systemic barriers (i.e., women should overcome sexism because they have personal agency and the individual choice to do so). I contend that the CAF is using empowerment messaging in some of their public messaging, and I propose that the negative effects of empowerment messaging will generalize to the CAF context. In two experiments (total N = 812), exposure to empowerment messaging from a CAF video directly increased the burden placed on women to solve gender inequality in the CAF and indirectly predicted more blame placed on women for causing gender inequality in the CAF. This research suggests that institutional empowerment messaging meant to motivate and inspire women may in fact harm women. The CAF and other institutions struggling with gender inequality should avoid using empowerment messaging in their public communications to prevent harming women, especially with their focus on increasing the number of women in their organizations.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.001 |
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".