Painted Red: The Soviet Interpretations of Hume’s Epistemology
Bibliographic record
Abstract
Abstract: In this article, I discuss the ambiguous nature of the Soviet interpretations of Hume’s theory through the example of the leading Soviet researcher, Igor Narskii, and his book The Philosophy of David Hume . Generally, his approach conforms to the stereotypes about the Soviet history of philosophy. While analyzing topics of perception and reflection in the Treatise , Narskii accuses Hume of terminological ambiguity and suggests Marx’s notion of “disposition” as an optimal way to describe human perception. However, some of Narskii’s ideas are more intriguing. He claims that Hume does not distinguish between ideas and notions in his “representationalist” theory of abstraction and concludes that the role of language and social ties in forming general ideas was neglected in Hume’s theory. This resonates with contemporary trends in Hume studies. I will show how contemporary scholars answer those accusations by implementing the linguistic reading of Hume.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".