Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This essay explores a problem for Nyāya epistemologists. It concerns the notion of pramā . Roughly speaking, a pramā is a conscious mental event of knowledge-acquisition, i.e., a conscious experience or thought in undergoing which an agent learns or comes to know something. Call any event of this sort a knowledge-event . The problem is this. On the one hand, many Naiyāyikas accept what I will call the Nyāya Definition of Knowledge , the view that a conscious experience or thought is a knowledge-event just in case it is true and non-recollective. On the other hand, they are also committed to what I shall call Nyāya Infallibilism , the thesis that every knowledge-event is produced by causes that couldn’t have given rise to an error. These two commitments seem to conflict with each other in cases of epistemic luck , i.e., cases where an agent arrives a true judgement accidentally or as a matter of luck. While the Nyāya Definition of Knowledge seems to predict that these judgements are knowledge-events, Nyāya Infallibilism seems to entail that they aren’t. In this essay, I show that Gaṅgeśa Upādhyāya, the 14th century Naiyāyika, solves this problem by adopting what I call epistemic localism , the view that upstream causal factors play no epistemically significant role in the production of knowledge.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it