Bibliographic record
Abstract
In previous work, Susanna Siegel has offered novel and probing arguments for what she calls the ‘rich content view’ of perceptual (specifically, visual) experience, according to which experience is capable of representing a richer array of properties than philosophers and psychologists often give it credit for. That is, a particular visual experience can represent not only a cluster of low-level properties relating to shape, colour, motion and illumination but can also represent, for instance, the decidedly higher-level property of being John Malkovich.1 A natural ally of the rich content view is the cognitive penetration thesis, that is, the view that the contents of perceptual experience can be affected by our beliefs and other cognitive states.2 In The Rationality of Perception, Siegel draws attention to the epistemological consequences of the cognitive penetration thesis.3 It is easy to see how cognitive penetration might give rise to epistemological concerns. We often appeal to perceptual experience to justify our beliefs. My belief that the soup is too salty is based on my perceptual experience of its tasting too salty. Ordinarily, I am not even brought to reflect on the justificatory link between the experience and the belief.
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.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".