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Enjoying the Unbeautiful: From Mendelssohn's Theory of “Mixed Sentiments” to Kant's Aesthetic Judgments of Reflection

2009· article· en· W1974199069 on OpenAlexaff
Alexander Rueger

Bibliographic record

VenueJournal of Aesthetics and Art Criticism · 2009
Typearticle
Languageen
FieldArts and Humanities
TopicPhilosophical Ethics and Theory
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSublimeBeautyPleasureAestheticsPerfectionIrrational numberPhilosophyPerceptionEpistemologyAesthetic experiencePsychology

Abstract

fetched live from OpenAlex

The development of the philosophical discipline of aesthetics in the eighteenth century has sometimes been characterized as an attempt to acknowledge that in the enjoyment of beautiful objects we experience something that cannot be captured by the discursive tools of reason. Aesthetic experience, according to this view, is the refuge of the irrational, the particular, the ineffable individual.1 Butanother view holdsby the very attempt to establish aesthetics as the science of the analogon rationis in Alexander Baumgarten's Aesthetica, beauty was in fact integrated into the domain of the rational: aesthetics in this sense effectively meant a of an area outside of reason's domain by reason itself.2 This applies, of course, in particular to rationalist attempts to integrate the experience of beauty into a philosophical framework in which the relevant pleasure has to be accounted for as a consequence of the perception of some perfection or other. The aesthetic experience of the unbeautifulthe sublime, the tragic, the horribleby contrast, seemed to be more recalcitrant to such colonization and has hence been taken by many scholars as philosophically more interesting and significant than the experience of beauty. Jean-Francis Lyotard, for instance, has tried to show in the case of Immanuel Kant how problematic the integration of the sublime into a philosophical system is.3

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.408

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.045
GPT teacher head0.277
Teacher spread0.232 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2009
Admission routes1
Has abstractyes

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