Bibliographic record
Abstract
This book would not have been possible without the help and guidance of a great number of people.My first thanks must go to the Terapanthi Jain community for its immense generosity.A special thanks goes to the late Ganadhipati Guru Dev Tulsi, whose support opened so many doors that would otherwise have remained closed to an outsider.I also wish to thank Acharyasri, Sadhvi Kanak Prabha, Niojikaji, Samanji, the Sadhvis, Samanis, Mumukshus sisters, Munis, and Samans; without their warm welcome this research would not have been possible.I am deeply indebted to my dear friends in the Samani order who made my experience such a memorable one.I will always remember your good humour, patience, and endless kindness.Dhanyavad!I am privileged to have had the affection and guidance of Professor A.N. Pandeya, who treated me as a family member during my brief sojourns in Delhi and provided me with invaluable suggestions in the early stages of my fieldwork.Of those people in Canada to whom I owe great thanks, four stand out in particular.First is Michael Lambek, whom I was
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.343 | 0.250 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".