Bibliographic record
Abstract
Compared to armies and navies, which have existed as professional fighting services for centuries, the technology that makes air forces possible is much newer. As a result, these services have had to quickly develop methods of preparing aviators to operate in conditions ranging from peace or routine security to full-scale war. The first book to address the history and scope of air power professionalization through learning programs, Educating Air Forces offers valuable new insight into strategy and tactics worldwide. Here, a group of international experts examine the philosophies, policies, and practices of air service educational efforts in the United States, France, Italy, Germany, Australia, Canada, and the UK. The contributors discuss the founding, successes, and failures of European air force learning programs between the Great War and World War II and explore how the tense Cold War political climate influenced the creation, curriculum, and results of various programs. They also consider how educational programs are adapting to soldiers' needs and the demands of modern warfare. Featuring contributions from eminent scholars in the field, this volume surveys the learning approaches globally employed by air forces in the past century and evaluates their effectiveness. Educating Air Forces reveals how experiential learning and formal education are not only inextricably intertwined, but also necessary to cope with advances in modern warfare.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.031 | 0.013 |
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".