Bibliographic record
Abstract
I was surprised that any one would want me to be a respondent for a panel which was primarily concerned with methodological approaches to Hindu-Christian Studies, my interest being mainly philosophical Sanskrit texts. However as a Hindu and having lived in India for most of my adult life I was interested to learn more about the way academics deal with this issue, which really is one that is fraught with many complications. Harold Coward, who gave a retrospect of the Society at the beginning of the session, did indeed give me a glimpse of the topics and methodology followed in this discipline. Harold's contribution over so many years in the area of Hindu-Christian (H-C) studies needs to be acknowledged and, in a certain sense, as I said at the meeting itself, Harold can be compared to the grandfather figure of Bhisma (Bhisma-pitamaha) in the Mahabharata (MBH).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.131 | 0.112 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.015 | 0.061 |
| Scholarly communication | 0.025 | 0.022 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.013 | 0.017 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".