Unconventional airpower: how non-state actors used aerial drone capabilities
Bibliographic record
Abstract
Amid the debate as to whether violent non-state actors (VNSAs) may use drones to level the playing field against their stronger adversaries, scholars have overlooked which types of tactical capabilities VNSAs could use and how they can integrate them. In developing metrics for gauging success with reference to theories of airpower, we analyze four cases – the Islamic State, the al-Qassam Brigades, the Three-Brotherhood Alliance, and the People Defence Forces – to examine how different actors employ drones to achieve specific gains on the battlefield. We find that drones provide short-term tactical advantages to VNSAs, mostly by catching an adversary off-guard with new tactics or by conserving their own manpower. Drones do give VNSAs access to airpower, something that had been largely exclusive to states, but they use such access mostly in support of insurgency tactics. Fit for a technology as dynamic as tactical drones, we offer a framework that scholars could use for future analysis of asymmetric drone warfare.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".