Rebooting military ethics from moral injury
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it