Commentary and Multilingualism in the Ottoman Reception of Texts: Three Perspectives
Bibliographic record
Abstract
Introduction by Jeannie Miller As a scholar of medieval Arabic literature, I often find myself in a predicament. In the manuscript sources underpinning my research, I find comments, variants, shifts in chap-ter structure, and other scribal and scholarly interpretations. These accretions are often insightful and significant, and they sometimes relate in interesting ways to the history of modern scholarship. But as I go about constructing arguments and textual interpre-tations, wishing to integrate the insights of the tradition or provide a critical history of prevalent understandings, I am uncertain about how to contextualize this rich and vibrant early modern cultivation of medieval Arabic texts. Throughout the nineteenth and most of the twentienth century, a “decline paradigm,” now widely critiqued, discouraged the study of Islamicate history and culture during the roughly five centuries between the Mongol conquest of Baghdad in 1258 and Napoleon's invasion of Egypt in 1798. This pall has dissipated in recent decades, leading to a burst of vibrant scholarship on early modern Islamic societies, and in the past few years we have seen increased scholarship on Ottoman Arabic literary production in particular. When it comes to the early modern Arabic language arts (philology and literature), the Maghreb and the majority Arabic-speaking provinces of the Ottoman empire have been the main focus of scholarship, though recently Arabic literary production and philology in majority Turkish-speaking territories and the Persianate Safavid and Mughal empires have received some limited attention. But Dana Sajdi's remarks about Ottoman intellec-tual history apply just as well to studies of Ottoman-era Arabic literature: scholars have “not sought to link these intellectual trends to those occurring at the imperial centre, probably because most modern scholars are proficient in either Arabic or Ottoman, but rarely in both.” The prolific and important body of Turkish-language scholarship is “not widely known internationally,” among Arabic literary scholars or Islamic Studies schol ars. National linguistic boundaries still often shape researchers’ perspectives, despite the widely acknowledged multilingual character of early modern Islamicate literary cul-ture, which worked among Arabic, Persian, and Turkish. When it comes to the Arabic language arts, the situation is complicated by an exaggerated interpretation of the Otto-man perception that Persian was a literary language, whereas Arabic was the language of the sciences, especially the religious sciences.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".