Defending Iran: From Revolutionary Guards to Ballistic Missiles <i>by Gawdat Bahgat and by Anoushiravan Ehteshami</i>
Bibliographic record
Abstract
Much has been written on Iran’s domestic politics and foreign policies to help guide policymakers. Nevertheless, the country often remains poorly understood. This book by Gawdat Bahgat and Anoushiravan Ehteshami, both respected experts, offers a timely and relevant synthesis of what is known and not known about Iran’s defense policy; it should become essential reading for scholars, students, and practitioners. The book covers extensive grounds. It dives into the domestic sources of Iran’s conduct, especially ideology and threat perceptions. It explains the structure of Iran’s security forces and, usefully, of its military-industrial complex—the target of much investment before and after the 1979 revolution. It also offers detailed chapters on the tools in Iran’s portfolio to deter its adversaries and project its power, especially its missile and space programs, its cyber capabilities, and its naval and drone forces. Several themes emerge. Often neglected in public debates, one of these is the issue of continuity in Iranian foreign policy. Much changed after 1979; Iran suddenly went from close partner to adversary to the United States. But as Bahgat and Ehteshami clearly explain, it is possible to identify important elements of continuity because of the permanent impact of structural pressures; the authors show that Iran is, in broad terms, a normal state in this sense. Iran was and still is, notably, an ambitious but insecure regional power surrounded by a high number of actual or potential competitors.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".