Russia’s rising military and communication power: From Chechnya to Crimea
Bibliographic record
Abstract
Most scholars working on Russia’s use of strategic narratives recognize the importance of the Russian state. Nevertheless, the authors argue that much of the attention on strategic narratives has given insufficient appreciation for how Russia has developed its military and media policies in a coordinated manner: learning from its mistakes and failures as it went along, and becoming more efficient each time. In making their case, they examine three theatres of Russian military activity and their accompanying media coverage: the wars in Chechnya in 1994–1995 and 1999–2000; war with Georgia in 2008 over the separatist territories of South Ossetia and Abkhazia; and Ukraine, especially Crimea, since 2014. The Russian leadership addressed the shortcomings on each occasion, with the news media being increasingly weaponized as time went on. The authors argue that scholars should see Russia’s evolving uses of those military and media power resources as part of a single strategic process. How the Russian state goes about its media policy can accentuate the military intervention for better or for worse as far as its image is concerned.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".