Cyclones in cyberspace: Information shaping and denial in the 2008 Russia–Georgia war
Bibliographic record
Abstract
Abstract While the rhetoric of cyber war is often exaggerated, there have been recent cases of international conflict in which cyberspace has played a prominent role. In this article, we analyze the impact of cyberspace in the conflict between Russia and Georgia over the disputed territory of South Ossetia in August 2008. We examine the role of strategic communications, information operations, operations in and through cyberspace, and conventional combat to account for the political and military outcomes of the conflict. The August 2008 conflict reveals some emergent issues in cyber warfare that can be generalized for further comparative research: the importance of control over the physical infrastructure of cyberspace, the strategic and tactical importance of information denial, the emergence of cyber-privateering, the unavoidable internationalization of cyber conflicts, and the tendency towards magnifying unanticipated outcomes in cyber conflicts – a phenomenon we call ‘cyclones in cyberspace’.
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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 | 0.003 |
| 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 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".