Bibliographic record
Abstract
This book began after a series of conversations with Ayesha Jalal on the nature of reform and religious life on the campus of Tufts University over a decade ago. An extension, of sorts, of the many discussions with her on the topic since my days as her PhD student twenty years ago, I was struck by the need to study religion in the era of nineteenth-century reform not with an eye toward communalism that developed later in time, but to the many meanings of religion, especially comparative religion, in the nineteenth century. Another important moment emerged at a 2014 conference on the occasion of the anniversary of the Centre for South Asian and Indian Ocean Studies at Tufts University, convened by Sugata Bose and Ayesha Jalal. In a panel on religion in nineteenth-century India in which I discussed religious reform, I fell into a long conversation with the great scholar Professor Susannah Heschel on various aspects of religion, history, and approaches to empire. This chance encounter led me to think seriously about religion's many historical guises. For that generative discussion and for ongoing friendship and fellowship, I am grateful. Ideas developed in this book grew after the ‘Religion and Its Others: Power, Sovereignty, and Politics in Indian Religions Past and Present’ workshop, funded by the Shastri Indo-Canadian Institute at the University of Victoria in March 14–15, 2019. This workshop featured the generative work of Rinku Lamba, A. Azfar Moin, J. Barton Scott, Shruti Patel, Brian Hatcher, Uday Chandra, and Ramesh Bairy.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.222 | 0.130 |
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".