Bibliographic record
Abstract
Studies of violence relating to the Middle East have sometimes done more harm than they have explained. Like the intended effects of the U.S. military's doctrine of “rapid dominance,” compared by its proponents to “tornadoes, hurricanes, earthquakes . . . famine and disease,” violence in the Middle East would appear to be “incomprehensible,” though less to “the people at large” who are affected by it than to its prolific theoreticians. Over the past two decades, much of the literature on the region as a “cauldron of war”—generating five times its share (by population size) of total global conflict since the mid-20th century—has tended to update and propagate well-known mythologies of primitivism, authoritarian personalities, and ancient hatreds. The significance of such mythopoeia has been its capacity to realize, at least in part, the conditions of its own truthfulness by shaping perception and policy, framing and enabling the infliction of a new wave of warfare on the region. Much contemporary writing on post–Cold War global crisis, the geopolitics of instability, regional conflict, and the future of warfare has not only signally failed to understand the dynamics of the Middle East but has actively contributed to the spread of violence 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.042 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".