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Record W1917738955

Where Nature Goes: Garden, Music and Emily Dickinson’s Poetry

2015· article· en· W1917738955 on OpenAlexvenueno aff
Jiangyue Chen

Bibliographic record

VenueStudies in literature and language · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicKantian Philosophy and Modern Interpretations
Canadian institutionsnot available
Fundersnot available
KeywordsPoetryNatural (archaeology)HeavenBeautyArtAestheticsLiteratureIdentity (music)History
DOInot available

Abstract

fetched live from OpenAlex

The understanding of the relationship between nature and art deeply influences our environment. With rich images of nature in her poems, Emily Dickinson’s identity as a gardener is a necessity to her literary career, for all her observations from nature occur in her garden. As a gardener and a hermit, her “nature”I s all about her garden, which is a curious phenomenon worthy of discussion, for garden is an existence between pure nature and artificial creation: All plants are natural but people can choose them and hybrid them. Emily plans her garden as natural as it could be, and in her poem she also says that Eden is more beautiful, so her attitude for “nature” and “art” is obvious, she clearly expresses that the ideal nature excels nature, and nature excels art. In her more natural garden, there is many images with emphasis again her attitude towards nature and art, for instance, music as an image can also show her preference between nature and art. In spite of the beauty of artificial music, the natural music like bird songs is more beautiful, and the music of the heaven excels the former again. Emily Dickinson’s ecological view of nature and art provides another interesting angle to look at ecological literature, and it is necessary to regard her as an ecological writer.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.014
Scholarly communication0.0040.003
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.034
GPT teacher head0.297
Teacher spread0.263 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2015
Admission routes1
Has abstractyes

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