The Middle Eastern Cold War: Unique Dynamics in a Questionable Regional Framework
Bibliographic record
Abstract
One of the more prominent themes to emerge from this roundtable is the desire to integrate the history of the modern Middle East with broader trends in international history, particularly with regard to the recent emphasis on “decentralizing” and “globalizing” the Cold War narrative. My own research interests are consistent with this approach, as one of the central concerns of my current project is to show how Algeria's revolutionary nationalists defied the regional categories imposed on them from the outside by pursuing overlapping diplomatic initiatives under the rubrics of Maghribi unity, African unity, Arab unity, Afro-Asianism, and Third Worldism. After independence in 1962, the Algerian foreign ministry's main geographical divisions differed significantly from those used by the U.S. State Department—and most history departments’ hiring committees—by dividing the world into “the West,” “the Socialist Countries,” “the Arab World,” “Africa,” and “Latin America/Asia.” These categories were the product of both practical considerations and ideological/identity politics on the part of Algeria's new leaders, and to my mind suggest that the “Middle East” may itself be a particularly arbitrary and misleading geographical framework, even in comparison to other parts of the developing world where European imperialism exerted a heavy cartographical influence.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".