Kenneth M. Cuno and Terence Walz, editors. Race and Slavery in the Middle East: Histories of Trans-Saharan Africans in Nineteenth-Century Egypt, Sudan, and the Ottoman Mediterranean.
Bibliographic record
Abstract
The last few years have seen a resurgence of scholarly interest in enslavement. Previous waves of interest centered mostly on slavery in antiquity (biblical times, Greece, Rome) and the modern North Atlantic world, with special emphasis on U.S. plantation bondage in the antebellum South. Later works on the Atlantic world incorporated slavery in Brazil and the Caribbean islands and offered a wide array of comparative studies. The neglect of non-Atlantic systems of enslavement began to be rectified in the last decade or so, reaching full bloom around the bicentennial of the proclamation of the 1807 Abolition Act regarding the Atlantic slave trade. The current cycle of scholarship, thus, seems to accept the notion that going beyond the classical modes of enslavement might provide important insights into one of the most baffling phenomena in human history. This volume, edited by Kenneth M. Cuno and Terence Walz, is part of a growing body of literature on enslavement in non-Atlantic societies and focuses on Egypt, Sudan, and the Ottoman Mediterranean in the nineteenth century. For the growing group of dedicated scholars who have been working for the past quarter-century on the history of enslavement in the Ottoman Middle East and North Africa, this is no doubt a happy occasion.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".