Ahmed El Shamsy. <i>Rediscovering the Islamic Classics: How Editors and Print Culture Transformed an Intellectual Tradition</i>.
Bibliographic record
Abstract
All scholarly traditions have their canonical works. In Rediscovering the Islamic Classics, Ahmed El Shamsy explores how particular works of Islamic thought, such as Ibn Khaldun’s Muqaddima, became the familiar and well-established classics recognizable today. El Shamsy offers a compelling history of reformers and intellectuals who, over decades in the nineteenth and twentieth centuries, labored to locate, edit, and print early and often forgotten texts. He situates these efforts and the rich debates that surrounded them in the social and cultural changes of the period often referred to as the Arab nahda, “the awakening.” The first two chapters explore the dearth of manuscripts in Arab lands before the “rediscovery” of these classics. Ottoman expansion in the sixteenth century reduced great learning centers, like Cairo and Damascus, to provincial capitals that lost many of their prized book collections to the imperial capital, Istanbul. Environmental factors such as humidity and insects damaged many manuscripts too. Wars, the economic decline of endowed learning centers and libraries, and the scattering of old collections made it difficult for scholars to consult or even find those earlier works. Furthermore, in the late eighteenth and nineteenth centuries, Orientalists, with substantial institutional and financial support, transferred thousands of Arabic manuscripts to European libraries and universities.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".