Social and Leadership Factors Influencing Moral Decision Making in Canadian Military Operations: An Annotated Bibliography
Bibliographic record
Abstract
Abstract : Military missions for the Canadian Forces (CF), including asymmetrical warfare and counter insurgency operations, have become increasingly complex and, as such, there is a greater probability that soldiers of all ranks will confront moral and ethical decisions. As moral and ethical decision making is both challenging in a military operational context (Thomson, Adams, & Sartori, 2006) and argued to be a social activity (Haidt, 2001), military personnel will undoubtedly look to others, including leaders and peers, for guidance or support to make the right choices. To this end, an annotated bibliography was conducted to highlight probably social and leadership factors that will influence moral and ethical behaviour. A number of social factors were identified, including moral disengagement mechanisms (such as displacement of responsibility, diffusion of responsibility, and dehumanization); ingroup/outgroup differentiation; group identity; group cohesion and loyalty; compliance and conformity; false consensus effect; and normative social influence (or social contagion). Leadership factors included the impact of different kinds of leadership (e.g., transactional, transformative, ethical, etc.), ethical role modelling, and leadermember interaction. This report also included a preliminary data analysis of CF subject matter experts (SMEs) first-hand accounts of moral and ethical decisions made in operations to highlight any social and leadership factors that had an influence on this process. It provided some initial insights into some of the social and leadership factors influencing moral decision making, including the false consensus effect, top-down normative social influence, the role of others in moral judgement, moral disengagement, compliance through leader participation, group loyalty and ingroup policing policies, and ingroup/outgroup differentiation. Moreover, participant responses showed strong evidence of ethical leadership and how this impacte
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.023 | 0.045 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".