Devotion in Colonial Islam: Representations of Muḥammad in Urdu Sīra (1842–1914)
Bibliographic record
Abstract
This dissertation is a study of three compositions of the sīra (biography of the Prophet Muḥammad), a centuries-old tradition by which Muslims have recorded the memory of their beloved Prophet and expressed religious devotion. The texts are the Jilāʾ al-Qulūb bi-Ẕikr al-Maḥbūb (1842) and al-Khut̤bāt al-Aḥmadīyah (1870) by Sir Sayyid Aḥmad Ḵẖān (1817-98), and the Sīrat al-Nabī (1914) by Shiblī Nuʿmānī (1857-1914). These little-studied Urdu works are examined in light of the significant political changes that occurred in nineteenth-century North India alongside new movements to vernacularize religious literature and rapid developments in print technology. By studying the forces that the authors encountered in composing Muḥammad’s life story that could, on the one hand, satisfy a modernizing Muslim population and, on the other, respond to Orientalist scholarship on Islam this dissertation examines the innovations that were introduced to convey the age-old sīra tradition in Urdu. Chapter One outlines the significant changes in the nineteenth-century experiences of Islam in South Asia and their impact on the traditional functions performed by the sīra. The changes included the reorganization of religious institutions, adjustments to the significance of the figure of Muḥammad, and modifications in the understanding of the concepts of religion, historiography, and biography. Chapter Two presents an analysis of the Jilāʾ, and argues that in addition to being a devotional work it displays reforms to the genre which challenge a predominant historiographical claim that these types of religious reforms were not instituted until after the 1857 Rebellion. Chapter Three presents an analysis of the Khut̤bāt, including an examination of how time is represented in Sir Sayyid’s writings. The chapter argues that Sir Sayyid’s conception of time disrupts the author’s aims of reconfiguring the representation of the figure of Muḥammad, for instance by the appearance of supernatural characteristics, not bound to place and time. Chapter Four examines Shiblī Nuʿmānī’s Sīrat al-Nabī by illustrating the challenges that “salvation history” presented to the composition of a sīra in the modern era and also addresses the role that conceptions of gender played in Nuʿmānī’s depiction of Muḥammad in narratives related to the Prophet’s wives.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".