Bibliographic record
Abstract
In The Dragoman Renaissance E. Natalie Rothman traces how Istanbul-based diplomatic translator-interpreters, known as the dragomans, systematically engaged Ottoman elites in the study of the Ottoman Empire—eventually coalescing in the discipline of Orientalism—throughout the sixteenth and seventeenth centuries. Rothman challenges Eurocentric assumptions still pervasive in Renaissance studies by showing the centrality of Ottoman imperial culture to the articulation of European knowledge about the Ottomans. To do so, she draws on a dazzling array of new material from a variety of archives. By studying the sustained interactions between dragomans and Ottoman courtiers in this period, Rothman disrupts common ideas about a singular moment of "cultural encounter," as well as about a "docile" and "static" Orient, simply acted upon by extraneous imperial powers. The Dragoman Renaissance creatively uncovers how dragomans mediated Ottoman ethno-linguistic, political, and religious categories to European diplomats and scholars. Further, it shows how dragomans did not simply circulate fixed knowledge. Rather, their engagement of Ottoman imperial modes of inquiry and social reproduction shaped the discipline of Orientalism for centuries to come. Thanks to generous funding from the Andrew W. Mellon Foundation, through The Sustainable History Monograph Pilot, the ebook editions of this book are available as Open Access volumes from Cornell Open (cornellopen.org) and other repositories.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.942 | 0.930 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".