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Record W2906765436 · doi:10.3968/8808

Eliot’s Approach to Ethical Poetry: The Waste Land

2016· article· en· W2906765436 on OpenAlexvenueno aff
Manar Hussein Ali Abu Darwish, Mohammed Ahmed Aqel Al-Widyan

Bibliographic record

VenueStudies in literature and language · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicModernist Literature and Criticism
Canadian institutionsnot available
Fundersnot available
KeywordsPoetryReading (process)PessimismAmbiguityHuman sexualityLiteratureDysfunctional familyAestheticsPure landEnvironmental ethicsSociologyHistoryPhilosophyLawPsychologyPolitical scienceArtEpistemologyGender studiesArchaeology

Abstract

fetched live from OpenAlex

This study aims at showing the ethical approaches in T. S. Eliot’s poetry. I argue that Eliot’s poetry is loaded with ethical approaches that characterized the era in which he lived. Also, the significance of this study arises from the fact that ethics have become buried in modern life. I, among others, feel we need it urgently these days to survive in a nice manner. When reading Eliot’s The Waste Land, we have come with a pessimistic reading of the poem. This reading applies to our life nowadays. Eliot imagines the modern world as a wasteland, a land that has been mixed with ambiguity, aridness, and destruction. This land, according to some critics, gives no indication of purity, which neither the land nor the people could visualize. In The Waste Land, various characters are sexually frustrated or dysfunctional, unable to cope with either reproductive or no reproductive sexuality.

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)0.003
metaresearch head score (Gemma)0.005
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 score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.020
Scholarly communication0.0050.005
Open science0.0010.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.001

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 head0.031
GPT teacher head0.294
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 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

Citations0
Published2016
Admission routes1
Has abstractyes

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