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Record W2742587188 · doi:10.5539/ells.v7n3p43

Animals in Walden

2017· article· en· W2742587188 on OpenAlexvenueno aff
Qin Liu

Bibliographic record

VenueEnglish Language and Literature Studies · 2017
Typearticle
Languageen
FieldComputer Science
TopicThoreau and American Literature
Canadian institutionsnot available
Fundersnot available
KeywordsCharacter (mathematics)EnvironmentalismSymbol (formal)Environmental ethicsAestheticsSociologyHistoryArtPhilosophyLawPolitical science

Abstract

fetched live from OpenAlex

Henry David Thoreau is a great American writer of transcendentalism and the pioneer of modern environmentalism. Being an ardent lover of nature, he devoted his entire life to studying the relationship between man and nature, and bequeathed a legacy of works in this field. He believed that nature was the symbol of spirit, and had a far-reaching influence on man and his character, and human beings should live harmoniously with nature for the long sustainable development. In Walden which is his masterpiece He endows the animals with human characteristics. Thereupon, Thoreau often describes the similarities between animals and people he comes across. People can be just as greedy and shallow as the marmot of the prairie, or as naughty and clumsy as red squirrels, or as lazy and cunning as chickadees, or as loyal as gundogs in Thoreau’s writings. Thoreau spent two years living a simple life at Walden on his own. He recounted in details the living habits of these animals, from woodchucks, loons to mice and hawks.

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: Empirical · Consensus signal: none
Teacher disagreement score0.030
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0060.005
Scholarly communication0.0060.004
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.002

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 head0.009
GPT teacher head0.291
Teacher spread0.282 · 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
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
Published2017
Admission routes1
Has abstractyes

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