CF Training for Moral and Ethical Decision Making in an Operational Context
Bibliographic record
Abstract
A half-day focus group discussion exploring current Canadian Forces (CF) training for moral and ethical decision making (MEDM) in an operational context was convened at CFB Kingston, Kingston, ON with six active Commissioned and Non-Commissioned Canadian Forces Officers who each had operational experiences involving moral and ethical challenges. Participants emphasized the importance of robust MEDM training for CF operational effectiveness. They identified five indirect means of training MEDM, which include promoting and instilling CF ethos and identity; learning from CF members operational experience and providing strong mentorship; evaluating and promoting individuals who consistently demonstrate high ethical conduct; systematizing MEDM knowledge transfer, and providing good post-MEDM support, including after action reviews, stress debriefings, and post-mission decompression opportunities. Participants also discussed specific requirements for training MEDM. For example, participants emphasized training MEDM is required at all rank levels and that it needs to occur regularly, as optimal MEDM cannot be promoted in a "two-day" course once every year. Lastly, participants also endorsed several direct means of training MEDM in an operational context. These included classroom case study training; live scenario-based training; and computer simulations.
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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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.010 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".