Rebooting military ethics from moral injury
Bibliographic record
Abstract
In 2022, members of the Five Eyes Mental Health Research and Innovation Collaborative recommended the integration of moral injury prevention into military leadership training and mission command, and the design of military ethics training to better prepare serving personnel for potentially morally injurious events. The Five Eyes is an intelligence alliance comprising Australia, Canada, New Zealand, the United Kingdom, and the United States. The Five Eyes Health Research and Innovation Collaborative comprises many of the world’s leading experts in moral injury who recognised the need to advance understanding of moral injury, including its moral/ethical dimensions. Their challenge is to take moral injury more seriously across all aspects of military life, including ethics training/education. This essay picks up the challenge from a Christian perspective. We look briefly at definitions of moral injury and examples of moral injury in workplaces, before re-visiting the origins of classic, Western theologically-rooted tradition of just war reasoning – in the experience of moral injury amongst serving military personnel. This essay reconsiders the origins of Western military ethics in Augustine’s conversations with Boniface. We begin where Augustine perhaps failed.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.076 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 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".