MétaCan
Menu
Back to cohort
Record W175382163

CF Training for Moral and Ethical Decision Making in an Operational Context

2006· article· en· W175382163 on OpenAlexaboutno aff
Michael H. Thomson, Kenneth L. Lee, Barbara Adams

Bibliographic record

VenueDefense Technical Information Center (DTIC) · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicTorture, Ethics, and Law
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)MentorshipTraining (meteorology)EthosAction (physics)PsychologyMedical educationEngineering ethicsApplied psychologyPolitical scienceEngineeringMedicine
DOInot available

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.805
Threshold uncertainty score0.412

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.086
GPT teacher head0.373
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueDefense Technical Information Center (DTIC)Same topicTorture, Ethics, and LawFrench-language works237,207