The Use of META in Junior Military Leadership Development
Bibliographic record
Abstract
This integrative literature review, set within the Canadian Armed Forces (CAF) context, examines the use of serious gaming simulations in military leadership development. This review addresses a leadership development gap in the military and assesses where serious gaming simulations (SGS) were deemed helpful in the armed forces’ human skills training. These human skills are particularly relevant in military contexts described as “operations other than war” (OOTW). An example of OOTW would be the Canadian Armed Forces’ support for long-term homes during the 2021-2022 COVID-19 pandemic. This paper aims to evaluate the efficacy of SGS in developing soldiers’ mindsets and capacity for skills development with a focus on human skills. Human skills refer to interpersonal and cognitive competencies, such as communication, decision-making under pressure, and collaboration (Touloumakos, 2020). The literature review focuses on identifying key themes related to the role of SGS in developing or enhancing leadership competencies relevant to the military. Specifically, it evaluates the impact of these SGS on collaboration, decision-making under time pressure, culture change, and communication skills within military teams.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".