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Record W4386461138 · doi:10.1080/10848770.2023.2253635

Joseph Brodsky and the Aesthetic Origins of Ethics

2023· article· en· W4386461138 on OpenAlexaff
Jeff Noonan

Bibliographic record

VenueThe European Legacy · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicVisual Culture and Art Theory
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsSensibilityFeelingBeautyAestheticsArgument (complex analysis)PoetryPhilosophyValue (mathematics)EvocationAmbiguityEpistemologyObject (grammar)Subject (documents)SociologyLiteratureArt

Abstract

fetched live from OpenAlex

In his Nobel Prize acceptance speech in 1987, the Russian-born American poet Joseph Brodsky argued that aesthetics is the mother of ethics. However, there is an ambiguity in his use of the term aesthetics. In the first part of this article, I distinguish between Brodsky’s narrow use of aesthetics, which refers to problems of beauty, and the broader sense, which refers to the cognitive function of sensibility and feeling. I then suggest that good sense can be made of the claim about the origins of ethics only if we employ the broader sense of aesthetics. The second part draws on examples from Brodsky and the American poet Jorie Graham to illustrate the ways in which the feelings generated by human sensuous receptivity transform the world from a meaningless physical system into meaningful sets of life-valuable relationships. The third part sharpens this conclusion by considering the conditions under which the world can appear to be meaningless. In the final section I link Brodsky’s poetic responsiveness to the world to John McMurtry’s philosophical analysis of “the felt side of being.” Poetic evocation and philosophical argument are two approaches to the same problem which, when brought together, provide a comprehensive explanation of why aesthetics is the mother of ethics.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.967
Threshold uncertainty score0.921

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.061
GPT teacher head0.280
Teacher spread0.219 · 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 designNot applicable
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

Citations0
Published2023
Admission routes1
Has abstractyes

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