Bibliographic record
Abstract
Abstract Perceptual experience supports the assignment of confidences in belief – doxastic confidences. To explain this fact, many philosophers appeal to Perceptual Indeterminacy, which holds that perceptual content can be more or less determinate. Others instead appeal to Perceptual Confidence, which says that perceptual experience supports doxastic confidences because it assigns confidences too. Morrison argues that a primary reason to favour Perceptual Confidence is that it is uniquely capable of accounting for bell-shaped doxastic confidence distributions; we call this the bell curve objection to Perceptual Indeterminacy. Here we show that two recent defences of Perceptual Indeterminacy, due to Nanay and Raleigh and Vindrola, fail to adequately address the bell curve objection. But we also argue that all is not lost for proponents of Perceptual Indeterminacy. They can counter the bell curve objection by embracing a third view, which we call Perceptual Noise.
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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 | 0.022 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.044 |
| Scholarly communication | 0.008 | 0.020 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 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".