Ella Fratantuono. <i>Governing Migration in the Late Ottoman Empire</i>.
Bibliographic record
Abstract
Governing Migration in the Late Ottoman Empire by Ella Fratantuono explores the Ottoman migration regime and the history of the past six decades of the Ottoman state from the perspective of mobility. This is an original and timely work given the recent and growing interest in the history of migration in the Ottoman Empire. The book makes several important contributions: It highlights the crucial connection between migration and state-building in the late Ottoman context, it explores the pivotal period of Ottoman reforms from the perspective of mobility, it demonstrates how migration contributed to the way the Ottoman state managed and understood its populations, and it makes a compelling case for studying the Ottoman migration regime alongside other better-known cases. The Ottoman experience becomes quite notable as the author brings out figures about migrations in the 19th and early 20th centuries. They demonstrate that the Ottoman state was in many ways comparable to other countries or regions in the world commonly associated with immigration. According to certain estimates, between the 1850s and the aftermath of the Balkan Wars (1912–13) more than 5 million people, mainly Muslims, arrived and settled in the Ottoman domains. Of course, it should be noted that only a fraction of them came from regions that had not been part of the empire, and most of them or their ancestors were Ottoman subjects who migrated from former Ottoman territories. In comparison, in the period 1820–1932 about 6.5 million Europeans settled in Argentina, more than 4 million moved to Brazil, and 5 million immigrated to Canada. Throughout the 19th and early 20th centuries, 6 million peasants resettled in Siberia. About 2 million Europeans moved to Cuba, Australia, and South Africa. In spite of comparable numbers, so far the Ottoman experience has remained largely overlooked in histories of migration and state-building. Fratantuono’s work aspires to fill this gap.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".