Iran's policy towards the Houthis in Yemen: a limited return on a modest investment
Bibliographic record
Abstract
For years, mounting instability had led many to predict the imminent collapse of Yemen. These forecasts became reality in 2014 as the country spiralled into civil war. The conflict pits an alliance of the Houthis, a northern socio-political movement that had been fighting the central government since 2004, alongside troops loyal to a former president, Ali Abdullah Saleh, against supporters and allies of the government overthrown by the Houthis in early 2015. The war became regionalized in March 2015 when a Saudi Arabia-led coalition of ten mostly Arab states launched a campaign of air strikes against the Houthis. According to Saudi Arabia, the Houthis are an Iranian proxy; they therefore frame the war as an effort to counter Iranian influence. This article will argue, however, that the Houthis are not Iranian proxies; Tehran's influence in Yemen is marginal. Iran's support for the Houthis has increased in recent years, but it remains low and is far from enough to significantly impact the balance of internal forces in Yemen. Looking ahead, it is unlikely that Iran will emerge as an important player in Yemeni affairs. Iran's interests in Yemen are limited, while the constraints on its ability to project power in the country are unlikely to be lifted. Tehran saw with the rise of the Houthis a low cost opportunity to gain some leverage in Yemen. It is unwilling, however, to invest larger amounts of resources. There is, as a result, only limited potential for Iran to further penetrate 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.004 |
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".