Bibliographic record
Abstract
Abstract One of the most frustrating features of modern jihadist insurgencies is their ability to endure and resurge, even after seeming defeats. What explains this jihadist resilience? In this paper, I present a new “boom–bust” economic theory for why jihadist groups can withstand serious losses, survive periods of decline, and then reclaim power. Using new evidence from Somalia, I demonstrate that jihadists learn how to adapt to fluctuations in their degree of territorial control, so that they can survive—and even thrive—during periods of decline. During a “boom” period, when jihadists enjoy a monopoly on force, they tax and govern as a proto-state. However, during a “bust,” when they are pushed out of power, jihadists shift their efforts to illicit business activities and insurgent warfare. When pressure abates, they again shift back to taxing and governing as a proto-state. This cyclical and long-term approach to order-making allows jihadists to adapt to changing battlefield conditions and survive serious setbacks. Jihadists establish their proto-states to varying degrees, governing in pockets and coves wherever and whenever the opportunities present themselves. They are as orderly as they can afford to be.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".