Bibliographic record
Abstract
Abstract Perception is a central means by which we come to represent and be aware of particulars in the world. I argue that an adequate account of perception must distinguish between what one perceives and what one's perceptual experience is of or about. Through capacities for visual completion, one can be visually aware of particular parts of a scene that one nevertheless does not see. Seeing corresponds to a basic, but not exhaustive, way in which one can be visually aware of an item. I discuss how the relation between seeing and visual awareness should be explicated within a representational account of the mind. Visual awareness of an item involves a primitive kind of reference: one is visually aware of an item when one's visual perceptual state succeeds in referring to that particular item and functions to represent it accurately. Seeing, by contrast, requires more than successful visual reference. Seeing depends additionally on meta‐semantic facts about how visual reference happens to be fixed. The notions of seeing and of visual reference are both indispensable to an account of perception, but they are to be characterized at different levels of representational explanation.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".