Landscapes of Control: The Geography of Counterinsurgency in the Sinai, Egypt
Bibliographic record
Abstract
This article presents a mixed-methods approach to understanding spatial conflict dynamics, with special attention to the embodied effects of violence and security on civilians. Our case study lies in the northeastern corner of Egypt’s Sinai Peninsula, where an affiliate of the Islamic State has waged a long-running insurgency against the Egyptian state and the local civilians. In response, Egyptian state forces have created a landscape of control featuring a network of earthwork fortifications designed to curtail movement that have become spatial loci for repeated violence. In this article, we investigate how the Egyptian military’s counterinsurgency tactics have affected conflict dynamics in two ways. First, we draw on the concept of biopower to narrate the effects of the state forces’ counterinsurgency landscape on the Sinawi civilian population. Given the hardships imposed on civilians by this landscape, we then seek to quantify its effectiveness in stopping the insurgency. The results of this analysis show that the state forces’ tactics have exposed civilians to a great deal of violence while failing to diminish conflict activity at a range of spatial scales.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".