The Ethics of Inclusion:Gender Integration, Equal Opportunity, and Sexual Assault in the Australian, British, Canadian and U.S. Armed Forces
Bibliographic record
Abstract
Women have played diverse roles in military campaigns for centuries, but it was only during the 20 th century that their work with or in the Australian, British, Canadian and US armed forces became increasingly formalised, important and permanent.Today official declarations abound avowing the indispensability of gender inclusiveness and diversity for military effectiveness in the 21 st century operating environment.Today's "militaries rely more and more on women and members of visible minority and Aboriginal groups to fill their ranks, rendering the recruitment, retention, and optimum employment of these members important to the success of the organizationfrom the perspectives of both operations and public accountability". 1 In some units and services women and members of non-white ethnic, non-Judeo-Christian religious or non-heterosexual minorities have been integrated effectively. 2However, equal opportunity and diversity policies have not been uniformly successful; for individuals who identify with several minority groups the situation can be especially precarious.High rates of sexual harassment, assault and rape in the military make these shortcomings glaringly obvious.That such offences happen at
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.095 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| 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".