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Record W2066988662 · doi:10.5539/ies.v6n7p225

Hepburn’s Natural Aesthetic and Its Implications for Aesthetic Education

2013· article· en· W2066988662 on OpenAlexvenueno aff
Chung-Ping Yang

Bibliographic record

VenueInternational Education Studies · 2013
Typearticle
Languageen
FieldArts and Humanities
TopicArt Education and Development
Canadian institutionsnot available
FundersNational Science Council
KeywordsAestheticsBeautyMetaphysicsMeaning (existential)Natural (archaeology)PerceptionSociologyEpistemologyArtPhilosophyHistory

Abstract

fetched live from OpenAlex

The world is rich in natural beauty, and learning how to appreciate the beauty of nature is an important part of aesthetic education. Unfortunately, the teaching of aesthetics is usually restricted to art education, especially in Taiwan. Students’ perceptual awareness of and sensitivity to the aesthetics of nature should be cultivated so that their lives may be enriched and their awareness of environmental issues may be raised. How the aesthetics of nature can be taught effectively remains a question. However, research in the contemporary aesthetics of nature provides insightful discussions and directions. This paper investigates the insights of aesthetician R. Hepburn and their implications for aesthetic education. Hepburn suggests that metaphysical meaning can be revealed by imagination in nature. According to this metaphysical model of the imagination, the essence of the “reality” of nature ought to be contemplated. We can imagine the meaning of life and experience a sense of oneness with nature, a co-presence of opposites, as well as sublimity and infinity by appreciating the beauty of nature. Accordingly, this paper discusses three implications for aesthetic education: 1) To teach students how to appreciate natural beauty should be included in aesthetic education; 2) To rich meanings of life by imagination in natural appreciation; and 3) To apply the metaphysical imagination model to teach students an appreciation of nature.

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)0.000
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: Empirical
Teacher disagreement score0.684
Threshold uncertainty score0.550

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.064
GPT teacher head0.360
Teacher spread0.296 · 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

Citations2
Published2013
Admission routes1
Has abstractyes

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