Determining the Required Training Capacity Within a Military Establishment
Why this work is in the frame
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Bibliographic record
Abstract
We address the problem of deciding how many positions to set aside for military recruits undergoing training. Within a cap on the total number of military members, we vary the ratio between positions allocated to the training pipeline versus those required in the trained effective establishment. This is done with the goal of determining the extent to which given ratios are sustainable. We use a Markovian model of the training pipeline, with parameters derived from historical personnel data. Through Monte Carlo simulation, we predict how often a given ratio allows the required trained force to be fully generated, as well as the surplus of trained personnel, it is expected to generate. We extend our previous work in this area by considering an alternative Human Resources policy that uncaps the training pipeline. Our modelling results have informed ongoing initiatives to optimize the force mix and structure of the Canadian Armed Forces.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it