Bibliographic record
Abstract
I examine competing views from Nyāya and Mīmāṃsā philosophers about the epistemology of absence, which asks how we learn that an object or property is absent. In particular, I examine Nyāya defenses of, and Mīmāṃsā challenges to, perceptualism, according to which we learn that an object or property is absent (abhāva) by perceiving (pratyakṣa) its absence. In the first chapter, I introduce cases of past absence (prāṅnāstitāsthala), purported counterexamples to perceptualism which involve agents learning in retrospect that an object or property was absent. I identify two groups of views about these cases: recollection views, according to which cases of past absence involve agents recalling negative information; and recollection failure views, according to which cases of past absence involve agents failing to recall positive information. I reconstruct two recollection views: a Bhāṭṭa view belonging to Uṃveka, and a Nyāya view belonging to Jayanta and Bhāsarvajña. I then examine Śālikanātha and Sucarita’s critiques of recollection views. In the second chapter, I examine recollection failure views. I introduce the Bhāṭṭa philosopher Pārthasārathi's view, following which I reconstruct the Nyāya philosopher Gaṅgeśa's critiques. Gaṅgeśa defends a similar view to Pārthasārathi's that mends its difficulties and secures a perceptualist explanation of past absence. According to Gaṅgeśa, agents learn that a recollectable (smaraṇārha) object or property was absent by inferring its past absence from failing to recall (asmaraṇa) that object or property. In the third chapter, I examine the Nyāya philosopher Raghunātha's attack on a condition according to which we are always aware of an absence as an absence of its counterpositive (pratiyogin), or its corresponding absent object or property. Gaṅgeśa defends this condition, showing that it is supported by a plausible thesis about the epistemology of relational properties and motivates the Nyāya defence of absence as irreducible to a positive. But Raghunātha identifies cases in which the condition fails. Finally, I provide a translation of, and commentary on, a selection from the Pramāṇapārāyaṇa by the Prābhākara philosopher Śālikanātha. I argue that he defends a reductionist metaphysics according to which absence reduces to an awareness-event (buddhi), and knowledge of absence thereby reduces to self-knowledge.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.006 | 0.019 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".