Are equitable physical performance tests perceived to be fair? Understanding officer cadets’ perceptions of fitness standards
Bibliographic record
Abstract
In the last few decades, the armed forces in Western countries such as Canada and the United States have accepted women into virtually all military occupations. Despite this, a growing body of research confirms that female service members face prejudiced treatment while conducting their work in these organizations that continue to be predominately masculine and male-dominated. In particular, women attending the Canadian Military Colleges (CMCs) experience gender-related conflicts arising from the dissimilar fitness test standards between male and female cadets. There have been, however, few studies that scrutinize the psychological mechanisms of these tensions. The aim of this study was to unpack the existing biased perceptions against women pertaining to physical fitness through ambivalent sexism, social dominance orientation, and right-wing authoritarianism. Officer and naval cadets (n = 167, 33.5% women) at the Royal Military College of Canada (RMC) completed survey measures. Indirect effect analyses showed that cadets who viewed the fitness standards to be unfair expressed more hostile rather than benevolent sexist outlooks against women, and these negative feelings were connected to greater levels of social dominance and right-wing authoritarianism. These results indicate that sexist beliefs, competitive worldviews, and authoritarianism are underlying attitudes that should be addressed by militaries striving to fully integrate women into their forces.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".