Plan for Moral and Ethical Decision-Making Program of Research
Bibliographic record
Abstract
DRDC Toronto has an ongoing commitment to investigating moral and ethical decision-making (MEDM) in Canadian Forces (CF) operations. Several projects had been previously funded through the Technology Investment Fund (TIF) awarded to the research team from the Command Effectiveness and Behaviour (CEB) section. This work has recently been extended into a 3-year Applied Research Program to further explore MEDM in operational contexts. This report proposes research ideas that could be explored in the context of this 3-year Applied Research Program (ARP). This research agenda is driven by two compatible motives: (1) to remain systematic and theory oriented and (2) to contribute to the CF's operational readiness in the domain of MEDM. The work on this research plan began with brainstorming a wide range of topics relevant to moral and ethical decision-making. Many of these topics derived from previous research exploring MEDM (Thomson, Adams, & Sartori, 2005; Thomson, Adams, & Sartori, 2006a; Thomson & Adams, 2007) and from focus group discussions with the DRDC Toronto MEDM Team. These research areas included person-based factors, team factors, contextual factors, situational factors, judgement and decision-making, emotion, and moral motivation and behaviour. Based on this initial mapping of the target domain, these broad areas were then narrowed to several focal areas, based on the following criteria: 1) their ability to contribute to the operational effectiveness of Canadian Forces; 2) their ability to contribute to the broader MEDM literature, and 3) on the skills and interests of the research team. Proposed focal areas include self-identity (person-based factor), team diversity (team factor), the role of collaborative processing (judgement and decision making), and the process of moral disengagement (moral motivation and behaviour).
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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 | 0.051 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.058 | 0.016 |
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".