Primordial Identities, Bounded Territories, and Contemporary Violence?: American Geopolitical Perspectives on the Middle East's Cultural Landscapes
Bibliographic record
Abstract
American perspectives on the Middle East often contend that the region's nation-states are comprised of clearly demarcated ethnic and religious groups whose identities remain static over time. Cultural features of the region are seen to be as durable as its physical features. These perspectives further maintain that when nation-state boundaries are incongruent with the boundaries of ethno-sectarian groups, civil unrest or violent conflict is inevitable. These assumptions are inaccurate because they employ outmoded colonial and Wilsonian views on social organization and can essentialize and/or depoliticize conflict. However, representations based on the assumptions of clearly bounded and static ethno-sectarian groups carry the advantages of making cultural landscapes legible, and thus amenable to geopolitical management. The goal of this project is to understand how ethno-sectarian territorial assumptions are employed in contemporary American views on the Middle East. To do this, I analyze three important sets of maps and texts which encapsulate contemporary American views on the region. The set of maps consists of easily accessible ethnographic maps of the Middle East. These maps are drafted, published, and made available by U.S.-based cartographers, journalists, government agencies, media outlets, and universities. The first set of texts focuses on the U.S. military's Iraq Troop Surge and are made up of American media coverage along with government, military, and think tank documents. The second set of texts focuses on the Arab Spring and are comprised of American media coverage, think tank reports, and academic commentary. My findings show that in most of these materials, it is assumed that the ethno-sectarian characteristics of the region can be depicted accurately, objectively, and completely in cartographic and textual representations. I conclude by asserting that problematic ethno-sectarian depictions are reinforced by the writing of prominent American foreign policy intellectuals. These depictions are important because they play roles in framing American geopolitical strategy and action in the region.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.024 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".