Experience Accumulation in Military Workforce Planning
Bibliographic record
Abstract
Workforce planning is at the core of military strategic planning. We focus on a critical aspect of many occupations in military workforces: the on-the-job training received by an inexperienced mentee under the supervision of an experienced mentor. We introduce the Experience Accumulation Module to allow the Athena Lite workforce modelling software to consider experience gained through on-the-job training. We compare four models and illustrate their differences with detailed results and analysis: (a) promotion requires minimum time in level; (b) promotion requires upgrade gained through physical resource usage; (c) promotion requires physical resource usage and Mentors; and (d) promotion through physical resource-constrained upgrade. We verify model implementation in Athena Lite by comparing our results from (a) through (c) with a well-known continuous workforce model. We then increase the complexity of our model in (d) and study the impact of resource levels on the upgrade process and the health of the population.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".