Town and Steppe in Ottoman Syria: Hostility, Exploitation and Cooperation in the Late Seventeenth and Eighteenth Centuries
Bibliographic record
Abstract
Abstract Late-seventeenth- and eighteenth-century sources from the Homs and Hama region in Ottoman Syria present contrasting portrayals of Bedouins. Taken together, these sources offer conflicting perspectives with respect to relationships between peoples of the towns and the steppe. On the one hand, literary sources typically portray Bedouins as antitheses of urban life, as savage wanderers who lived outside the norms of propriety and who collectively posed a threat to the wellbeing and property of settled people and of travelers. But on the other hand, legal sources portray Bedouins variously as targets of exploitation or taxation by urban-based governments; or as partners with urban people in contractual undertakings; or as imperial subjects who, like any others, would seek justice in the urban Sharīʿa courts. The article explores these differing characterizations, and seeks to explain the multifarious realities that different sources convey. It concludes by suggesting that relationships between town and steppe were on their way to becoming more institutionalized in the last years of the eighteenth century. This development foreshadowed documented nineteenth-century trends in which urban civil norms and institutions became noticeable in the lives of Bedouins who lived in proximity to towns and urban centers.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".