Saudi Arabia’s costly war in Yemen: a neoclassical realist theory of overbalancing
Bibliographic record
Abstract
Saudi Arabia faced multiple threats from Yemen in 2015: its southern neighbor had collapsed; a hostile sub-state actor, the Houthis, was entrenching itself along the border; and the presence of its rival Iran was growing. Responding was rational; it would have been sub-optimal for Riyadh to underbalance by doing little to counter the threat. Instead, however, Saudi Arabia overbalanced by launching a major air campaign and imposing a maritime and air blockade; as a result, it became bogged down in a costly war it cannot win. Why was this the case, and with what consequences? To answer this question, this article develops and applies a neoclassical realist theory of overbalancing. The first objective is nomothetic: to develop a theory of overbalancing, an important phenomenon neglected by the balancing literature. The second is empirical: to shed light on the Saudi decision to launch the war in Yemen.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".