Structuring Conflict in the Arab World: Incumbents, Opponents, and Institutions
Bibliographic record
Abstract
Structuring Conflict in the Arab World: Incumbents, Opponents, and Institutions , Ellen Lust-Okar, Cambridge: Cambridge University Press, 2005, pp. 279. For a very long time, the scholarship on Middle Eastern politics has suffered from scarce use of the analytical tools provided by the field of comparative politics. The result has too often been descriptive research in the anthropological style. Such studies lacked the rigour necessary for providing cumulative knowledge and theoretical insight. In recent years, however, an increasing number of scholars have been recognizing the value of complementing their in-depth knowledge of the region with appropriate social science theories. New theoretically oriented scholarship—produced by Mark Tessler ( Area Study and Social Science , Bloomington: Indiana University Press, 1999), Carrie Wickham Rosefsky ( Mobilizing Islam , New York: Columbia University Press, 2002), Quintan Wikorowitcz ( Islamic Activism , Bloomington: Indiana University Press, 2004), Eva Bellin ( Stalled Democracy , Ithaca: Cornell University Press, 2002), Lisa Anderson ( Transition to Democracy , New York: Columbia University Press, 1999), and a few others—filled such a need that, as a result of their publication, knowledge of Middle Eastern politics has taken a great leap forward since the early 2000s.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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".