Eva van Baarle and Peter Olsthoorn (2023) Resilience : a care ethical Perspective. Ethics and Armed Forces.
Bibliographic record
Abstract
Not only the direct physical experiences of deployment can severely harm soldiers’ mental health. Witnessing violations of their moral principles by the enemy, or by their fellow soldiers and superiors, can also have a devastating impact. It can cause soldiers’ moral disorientation, increasing feelings of shame, guilt, or hate, and the need for general answers on questions of right and wrong. Various attempts have been made to keep soldiers mentally sane. One is to provide convincing causes for their deployment, which risks an “end justifies the means” way of thinking. The good cause can provide a moral justification for horrible atrocities. Another method, introduced in the USA, Canada, and Australia, aims to strengthen military personnel’s resistance by promoting and maintaining a happy, optimistic state of mind through the use of positive psychology. Alongside making soldiers “morally fit” for all kinds of situations, the focus could also be on moral recovery and forgiveness. Such a care-based military ethics approach, aimed at mutual understanding and interdependence, could help soldiers handle the emotional impact of moral conflicts. This demands that military units reflect on their organizational culture and rethink oaths and codes of conduct that focus mainly on efficiency and readiness, as well as the soldierly self-image with its seemingly still deeply rooted warrior ethos. Today, resilience and positive psychology in the military is apparently mainly geared to assuring its soldiers’ readiness. An appropriate set of virtues and understanding of virtue ethics that are less centered on self-perfection and autonomy could point to a different form of character-building and lead to a better understanding of others.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.040 | 0.011 |
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".