Bibliographic record
Abstract
Abstract Seeing, Doing, and Knowing is a philosophical framework for thinking about sensory systems as active devices for data extraction B rather than, in the traditional way, as passive recorders of ambient energy patterns. Sensory systems are automatic sorting machines that assign real-world objects to classes. A sense feature is the property of belonging to such a class. A sensory experience, or sensation, is a label that the system uses in order to allow the organism access to the classifications that it has performed. This Sensory Classification Thesis (SCT), discussed in Chs 1–3, inverts the normally assumed relationship between sensory classes and sensations. Philosophers standardly hold that red is to be defined in terms of the sensation of red; here, sensations derive from sensory classes and are thus unsuitable for defining them. SCT is a simplification: some sensory systems order real-world objects in relations of similarity, and do not just put them into discrete classes (Chs 4–5). SCT makes sense of sensory specialization across species—different kinds of organisms employ different classification schemes to serve their idiosyncratic data-extraction needs (Chs 6–8). This leads to an output-driven account of sensory content. Sense features are defined in terms of their aptness for epistemic (not just sensorimotor) actions, and the content of sensations in terms of the features with which they are associated by an internal convention (Chs 9–11). This leads to a form of realism: sensory classifications are correct if the states of affairs in which they consistently occur are indeed right for the actions with which they are paired. Finally, the nature of object perception is explored: Chs 12–13 speculate about the psychological origins of sensory reference and of the feeling in perception that external objects are present (by contrast, for instance, with objects depicted in paintings and photographs).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.025 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".