Bibliographic record
Abstract
We can understand epistemic naturalism as the view that there are cases in which we are justified in holding a belief and cases in which we are not so justified, and that we can distinguish cases of one sort from cases of the other with reference to non-normative facts about the mechanisms that produce them. By my lights, Hume is an epistemic naturalist of this sort, and I propose in this paper a novel and detailed account of his epistemic naturalism. On my account, which I call the determinacy account, Hume characterizes epistemic justification in terms of the mind's feeling determined by the relation of cause and effect to move from one impression (or idea) to an(other) idea. I find a statement of this account, which Hume applies initially to what he calls the second system of realities, in Treatise 1.3.9. After rejecting other accounts of Hume's epistemic naturalism, I show how the determinacy account handles the cases Hume considers later in Treatise 1.3.
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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 | 0.000 | 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 | 0.001 |
| 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".