Violence-producing Dynamics of Fragile States: How State Fragility in Iraq Contributed to the Emergence of Islamic State
Bibliographic record
Abstract
In the post-Cold War era, “Jihadi-Salafi Groups” (JSGs) have emerged as significant “violence-making” organizations. Almost all JSGs have emerged in highly fragile states. The literature on the state fragility-terrorism nexus, by focusing exclusively on whether state fragility is a cause of terrorism or not, has failed to consider the broader impact of state fragility on the emergence of JSGs. The role of state fragility as a condition of the emergence of JSGs, in particular, is mostly overlooked in the literature. This paper, adding state fragility as a condition variable to the causal model of the rise of JSGs, fills this gap. The empirical basis of this research includes a single case study examining the relationship between state fragility in the post-Saddam Iraq and the formation of Islamic State (IS). By adding a new variable to the causal model of the rise of IS, this research makes a strong within-case inference concerning this case. Although the empirical basis of this research includes a single case study, the analytical framework developed in this paper has possible implications for studying a larger number of Jihadi-Salafi groups.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".