Bibliographic record
Abstract
Abstract Universals are properties that are shared by multiple objects. In classical South Asia, Brahmanical thinkers from Vyākaraṇa, Nyāya, Vaiśeṣika, and Mīmāṃsā text traditions were realists about universals, while most Buddhists were nominalists. In this paper, my aim is to reconstruct the early Nyāya-Vaiśeṣika theory of universals, with special emphasis on the arguments of the Nyāya philosopher Uddyotakara (6th century CE) against a Buddhist strand of resemblance nominalism. I show that Uddyotakara's contribution to this debate is twofold. First, he is possibly the first Naiyāyika to adopt a sparse theory of universals, a theory according to which it is necessary to posit only those universals which explain how objects resemble each other in the most fundamental or irreducible respects. On the other hand, he offers a few arguments for realism, which are explicitly motivated by a causal constraint on intentionality.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.033 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".