Bibliographic record
Abstract
Kant was never satisfied with the version of his “Refutation” published in 1787 (KrV, B 275-279). His dissatisfaction is already evident in the footnote added to the preface of the second edition of the Critique in 1787. As a matter of fact, Kant continued to rework his argument for at least six years after 1787. The main exegetical problem is to figure out who is the target of the “Refutation”: a non-sceptic idealist or a global sceptic of Cartesian provenance or both. In this last case, a related problem is to know whether either of them is the Cartesian sceptic of the first Meditation, the idealist sceptic of the second Meditation and first part of the third Meditation, or some other non-sceptic idealist. I present and defend a new reconstruction of Kant’s “Refutation” as a successful argument against Mendelssohnian idealism of Cartesian provenance. This defence is based on a simple logical sketch of the proof provided by Dicker, but essentially modified in the light of Dyck’s insight about Kant’s opponent. How shall I support my reading? First, by appealing to overwhelming textual evidence according to which the proof is of the existence of mind-independent things, showing that Kant’s main opponent is Mendelssohn’s idealism of Cartesian provenance. Finally, I support my reading by showing that Kant’s “Refutation” is doomed to fail against all forms of global scepticism, but is quite successful against Mendelssohn’s idealism.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.028 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".