Bibliographic record
Abstract
In this essay, I specify types of representational content that can be attributed to Kant’s account of representation. The more specific aim is to examine which of these types of content can be regarded as possible without the application of concepts. In order to answer the question, I proceed as follows. First, I show how intuition (in Kant’s sense) can be seen as providing indexical content independently of empirical concepts. Second, I show in what sense the generation of spatial content can be regarded as non-categorial. A key distinction is that a perceptual examination of an object can be understood as thoroughly sensible and particular, whereas a conceptual determination always grasps the object via its generalisable features. Third, I propose that the faculties of sensibility and understanding are not only separable in principle, but that their contributions remain in a certain sense separate in actual cognition as well. This is to say that a conceptual determination of an object does not entail that the object ceases to be non-conceptually available to the perceiver, which further suggests the autonomy of sensibility and its perceptual content. Finally, I raise difficulties in attributing non-conceptual representational content to Kant’s judgment-centered stance on representation and experience, only to emphasise how these difficulties easily lead to a misappreciation of Kant’s fundamental distinction between sensibility and understanding and their unique cognitive contributions.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".