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Record W4392238786 · doi:10.1177/07255136241227675

Practical aesthesis

2024· article· en· W4392238786 on OpenAlexaff
Rob Shields, Nicholas Hardy

Bibliographic record

VenueThesis Eleven · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Philosophy and Ethics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsEpistemologyPoliticsInterpretation (philosophy)SociologyEmpiricismConversationRelevance (law)Environmental ethicsPhilosophyAestheticsPolitical scienceLaw

Abstract

fetched live from OpenAlex

Aesthesis, the classical term for sensing and perceiving, is at the heart of innumerable problems that plague global society. The purpose of this article is to open a conversation on aesthesis. We survey the roots and relevance of aesthesis as a direct albeit contested relation and engagement with the world and with Others. From its pre-Socratic origins, aesthesis has been both a pragmatic, somatic concept, prompting a re-evaluation of the distinction between experience and abstraction. We trace its ongoing repression from Plato through ‘western’ theories of formal Aesthetics. Drawing on a relational interpretation of Protagoras’ aesthesis, we argue that modern pragmatists and radical empiricists, as well as more contemporary critics of the ‘colonization’ of aesthesis (Mignolo and Vasquez) by formal Aesthetics recognize and develop the relational and ethical aspects of aesthesis. We consider the role of the body, affect, and of the intangible or virtual qualities of aesthesis. The ethics of obligations (Weil) in the polis (Arendt) shows how aesthesis informs politics despite its repression in favour of moral and legal norms. We argue this is relevant to contemporary crises such as xenophobia and ecocidal climate warming.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.016
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0060.049
Scholarly communication0.0080.008
Open science0.0010.007
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0160.003

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 head0.041
GPT teacher head0.293
Teacher spread0.252 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2024
Admission routes1
Has abstractyes

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