Bibliographic record
Abstract
Extract Diana Dimitrova obtained her PhD in Modern and Classical Indology at the University of Heidelberg, Germany. She is Professor of Hinduism and South Asian Religions at the University of Montreal in Montreal, Canada. She is the author of Hinduism and Hindi Theater (New York: Palgrave Macmillan, 2016; paperback edition 2018); Gender, Religion and Modern Hindi Drama (Montreal: McGill-Queen’s University Press, 2008); and Western Tradition and Naturalistic Hindi Theatre (New York: Peter Lang, 2004). She is also the editor of Rethinking the Body in South Asian Traditions (London and New York: Routledge, 2021); Divinizing in South Asian Traditions. With Tatiana Oranskaia (London and New York: Routledge, 2018; paperback edition 2020); Imagining Indianness: Cultural Identity and Literature. With Thomas de Bruijn (New York: Palgrave Macmillan, 2017; paperback edition 2019); The Other in South Asian Religion, Literature and Film: Perspectives on Otherism and Otherness (New York and London: Routledge, 2014, paperback edition 2017); and Religion in Literature and Film in South Asia (New York: Palgrave Macmillan, 2010). She has also published over thirty articles on South Asian religions, literatures, and film. Her current research deals with the Radhasoami tradition and with cultural issues related to othering.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.392 | 0.277 |
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".