Bibliographic record
Abstract
With Imagining Karma, Gananath Obeyesekere embarks on the very first comparison of rebirth concepts across a wide range of cultures. Exploring in rich detail the beliefs of small-scale societies of West Africa, Melanesia, traditional Siberia, Canada, and the northwest coast of North America, Obeyesekere compares their ideas with those of the ancient and modern Indic civilizations and with the Greek rebirth theories of Pythagoras, Empedocles, Pindar, and Plato. His groundbreaking and authoritative discussion decenters the popular notion that India was the origin and locus of ideas of rebirth. As Obeyesekere compares responses to the most fundamental questions of human existence, he challenges readers to reexamine accepted ideas about death, cosmology, morality, and eschatology. Obeyesekere's comprehensive inquiry shows that diverse societies have come through independent invention or borrowing to believe in reincarnation as an integral part of their larger cosmological systems. The author brings together into a coherent methodological framework the thought of such diverse thinkers as Weber, Wittgenstein, and Nietzsche. In a contemporary intellectual context that celebrates difference and cultural relativism, this book makes a case for disciplined comparison, a humane view of human nature, and a theoretical understanding of "family resemblances" and differences across great cultural divides.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".