Introduction: Reflecting on the Global Impact of the RMA
Bibliographic record
Abstract
Nearly a quarter of a century after US-led coalition forces relied extensively on information technology, hi-tech precision weapons and joined-up military doctrine to comprehensively defeat Saddam Hussein’s Iraqi army in Operation Desert Storm, the concept, implications and legacy of the so-called Revolution in Military Affairs (RMA) remains both contested and indistinct. Indeed, and while the swift and impressive military victory in early 1991 ignited a widespread scholarly and policy debate about the transformative nature of modern technology in warfare,1 and became commonplace in strategic studies’ literature and policy guidelines throughout the 1990s and early 2000s, the military challenges of the past decade and a half have increasingly called in to question the efficacy of the RMA concept and its application. Conflict and intervention in Afghanistan, Iraq, Lebanon, Gaza, Mali, Libya, and most recently in Ukraine and against the group known as Islamic State (IS), have all pointed to a different type of challenge for modern militaries — and provided a difficult test for the RMA concept. As a result, the notion of an RMA has slowly disappeared from both academic and policy debate in the last decade and a half, as traditional and conventional conceptions of warfare have given way to asymmetric conflict and more complex use of force scenarios (at least that is, for the time being). However, RMA-based thinking and decisions continue to impact and affect the way modern militaries around the world approach and plan for future conflict, and many are still dealing with the effects of RMA-inspired decisions taken during the 1990s, and/or continue to base military planning at least partly on these ideas.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".