The Linguistic Factor in the Deterrence Conversation between Russia and the West: If and How Russians Understand the Meaning of “Deterrence?”
Bibliographic record
Abstract
Given Russia's aggressive actions in Ukraine and its hostile remarks toward its NATO neighbours, the question of why the credible deterrence posture of the Alliance is not producing the desired results is becoming increasingly important. Either Russian political leaders do not want to comply with the Western credible deterrence posture, or they do not understand what is expected from them. Linguistic factors (e.g., the ‘lost in translation’ effect) affect the effectiveness of the conversation on deterrence between Russia and the West, including NATO. The Russian language does not have one unambiguous equivalent for the Western term “deterrence.” There are several possible translations for deterrence in Russian, including sderzhivaniye, ustrasheniye, sderzhivaniye putem ustrasheniya, sderzhivaniye prinuzhdeniyem, as well as others. These terms are not synonymous, with each having its own different meaning and some being used in Russian military-political theory, where they refer to different concepts. The current study focuses on the most visible linguistic complications in the deterrence dialogue between Russia and the West. Based on the current study, it would often be more reasonable to talk to the Russian political and military elite about containment or coercion, which are more common and have clear, specific meanings in Russian.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".