Small Militaries and Operational Art: A Strange but Beneficial Pairing
Bibliographic record
Abstract
Abstract This chapter examines why and how small militaries have adopted operational art despite not possessing large enough forces to apply the concept in the manner it was originally intended. First, it establishes context by summarizing three large military operational art traditions – the German, Soviet, and American traditions. Second, it examines why small militaries have adopted the concept, positing that it is due to a mixture of interoperability and cultural reasons. While there are many potential ways to define “small militaries,” they are defined here as either comprising less than 100,000 personnel, or as fielding land combat forces of one division or less in size. By either definition, these militaries are smaller in scale than those required to apply operational art as conceptualized within the three large military traditions summarized. Third, three case studies examine how small militaries have adapted and applied the concept: those of Australia; Canada; and the Nordic and Baltic countries. Despite facing different strategic situations, the way these militaries have applied operational art has been very similar. All have taken a functional approach, which de-links operational art from its original emphasis on scale. These militaries have used this modified concept to enable them to operate more effectively alongside their larger allies, America in particular.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".