Airpower and Wagner Group’s expeditionary operations: exploring the “exceptionality”
Bibliographic record
Abstract
Though Russia’s Wagner Group and its expeditionary operations in the Middle East and Africa are the subject of extensive analysis, assessment, and scrutiny, consideration of the group’s relationship with airpower is largely missing. To close this gap, the article first contends that this relationship is in keeping with the Russian historical experience regarding different armed actors with state linkages. In some ways, Wagner Group exercises a degree of agency in terms of action and ownership while in others the Russian state has a greater hand. It then contends that airpower is an important force multiplier due to the strategic minimalism inherent in Russia’s utilization of Wagner Group and the group’s own quantitative restraints. Finally, the article stresses that, unlike the Western counterinsurgency (COIN) experience, and especially as it pertains to armed actors and airpower, the longstanding Russian approach involves human rights abuses, especially against civilian populations, for instrumental purposes. In the present day, Wagner Group mirrors this; it delivers violence from/through the sky, the sort that is integral to Authoritarian Conflict Management (ACM).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".