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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.020 | 0.000 |
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 teacher head, 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".