Military Acronyms: Notion, Categorization and Classification
Bibliographic record
Abstract
The article presents an overview of acronyms classification in English military terminology. Legal documents, official website of State Border Guard Service of Ukraine, journals and dictionaries related to military terminology were investigated in the research. Mostly used acronyms in English military terminology were classified and 718 definitions were singled out. Such methods as theoretical analysis, comparison, substantiation, and generalization, systematization of theoretical and practical material were used for the analysis of the scientific sources concerning military acronyms, comparing the military terminology acronyms according to their common usage, the selection of acronyms groups and their categorization correspondingly. As a result, the acronyms were divided according to their common usage into the following groups and subgroups: management acronyms (personnel, positions and organization acronyms), service acronyms (NATO and everyday activity acronyms), military operations acronyms (operational and communication acronyms), armament and military equipment acronyms (military equipment, weapons and ammunition acronyms), military medicine acronyms (medical training and medical terms acronyms), military law acronyms (documents, personnel and legal bodies’ acronyms), vehicles acronyms (marine vessels, land vehicles and military aircraft acronyms), nuclear area acronyms (missile, nuclear legislation and nuclear bodies acronyms), Armed forces organization acronyms (Army Command, Air Force, Navy and military intelligence acronyms). At the final stage of the study military terminology acronyms classification was developed and graphically presented using the MindManager program to categorize military acronyms according to their common usage.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".