Daily Experiences of Diverse Canadian Armed Forces Women Working in the Royal Canadian Navy
Bibliographic record
Abstract
Previous research has found that female soldiers in Belgium cope with daily experiences of social identity threat (i.e., a concern that an individual will be devalued because of a social identity that they hold) by self-group distancing (i.e., distancing themselves from, and avoiding, other women; Veldman et al., 2020). Self-group distancing is a harmful coping behaviour because it is associated with lower daily well-being and motivation. Further, the association between daily experiences of social identity threat and self-group distancing is mediated by daily belonging concerns, providing evidence that female soldiers distance from other women in order to try and fit in to the masculine domain of the military. We propose that women in the Canadian Armed Forces (CAF) working for the Royal Canadian Navy (RCN) will also cope with daily experiences of social identity threat by self-group distancing, ultimately undermining their wellbeing and motivation. We further propose that self-group distancing among CAF women will also be associated with increased psychological burnout and increased intention to leave the CAF, ultimately perpetuating the underrepresentation of women in the CAF. Additionally, we propose that the association between daily experiences of social identity threat and self-group distancing will be mediated by daily belonging concerns.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.003 | 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".