Bibliographic record
Abstract
Following the collapse of the Soviet Union, Islam has come to fill a pivotal conceptual role of an antithesis to the West, the self-described abode of liberal democracies and the rule of law. With the widespread rise of the Islamist movements during the last three or four decades, so-called Islamic law, or Sharīʿa, has increasingly occupied center stage in the languages and practices of politics – mainly in the Islamist camp itself, but also in the Western world. Popular narratives and a staggering array of quasi-scholarly accounts have distorted Sharīʿa beyond recognition, conflating its principles and practices in the past with its modern, highly politicized, reincarnations. This book is about distinctions; about what Sharīʿa – as doctrine and practice – represented in history; how it functioned within society and the moral community; how it coexisted with the body-politic; and how it was transformed and indeed appropriated as a tool of modernity, wielded above all by the nation-state. Although this book has, in many ways, been in the making for over two decades, it was written between 2004 and 2008, during which period much in my thinking on the subject continued to change and develop. Over time, this thinking and the resultant book became increasingly grounded in frameworks of enquiry beyond the field of law in general and Islamic law in particular. And like many other books, its several chapters and sections were written under variable conditions.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.223 | 0.112 |
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".