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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".