A gender-based analysis of recruitment and retention in the Canadian Army Reserve
Bibliographic record
Abstract
LAY SUMMARY In this qualitative study, 29 members of the Canadian Army Reserve were interviewed to investigate Canadian Armed Forces (CAF) recruitment and retention strategies. Studying member attitudes and participation in recruitment and retention led to original insights about the importance of community outreach, peer recruiting, and commitment on behalf of leadership when it comes to fostering a recruitment-focused culture. Participants pointed to camaraderie and the quality of training opportunities as significant considerations to improve retention, providing further validation to existing research on retention in reserve units. Using a gender-based lens, reservists were asked about the culture of the CAF, sexual misconduct, and issues facing under-represented groups. Participants felt the military was doing well meeting recruiting targets and that representation and mentorship were important tools to encourage women and members of under-represented groups to join. The answers regarding sexual misconduct were extremely consistent: most were surprised when hearing Reserve Force statistics on sexual misconduct, and many displayed low awareness of how to report incidents. Nevertheless, participants thought their units were better than others when it came to equity, diversity, inclusion, and preventing sexual misconduct, signalling these topics could be further examined in the reserves.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.018 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".