Bibliographic record
Abstract
Were there interactions between the development of Kant's aesthetics and the development of his moral philosophy? How did Kant view pleasure and displeasure and what role did they play in the formation of his system of the faculties? In this book, Alexander Rueger situates Kant's account of pleasure and displeasure in its eighteenth-century context, with special attention to Leibniz, Wolff, Crusius, and Mendelssohn. He traces the development of Kant's views on pleasure from the 1770s to his Critique of Aesthetic Judgment in 1790, and shows that throughout, Kant understood pleasure as the satisfaction of faculty interests. The significance of this theory for the completion of Kant's critical system in the third Critique is discussed in detail. Rueger's study illuminates both the role of pleasure and displeasure in Kant's thought, and their important connections to the power of judgment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".