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Record W2992856744

Eliot's Delving into the Oriental Wisdom: A Cross-Cultural Study

2011· article· en· W2992856744 on OpenAlexvenueno aff
Nasser Maleki, Mostafa Mirzaei

Bibliographic record

VenueStudies in literature and language · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicFoucault, Power, and Ethics
Canadian institutionsnot available
Fundersnot available
KeywordsLiteratureBuddhismDestiny (ISS module)PoetryHinduismPhilosophyOrder (exchange)HistoryArtAestheticsReligious studiesTheology
DOInot available

Abstract

fetched live from OpenAlex

The last century has witnessed an upsurge in literature triggered by the cross-cultural study of literary texts. This unprecedented event has transformed the various literary texts and genres that are being deconstructed to suit the changing times. T.S.Eliot has not been spared by the universalized world order. Eliot’s works are concerned with wisdom, the one which overlaps religion and is true for all men, at all times. By quoting from other sources, Eliot creates the deeper sense which forces the reader muse more effectively over the vital question of life and human destiny. In his poems, The Waste Land , and Four Quarters , and some of his plays, Eliot has withdrawn significant ideas from the Indian Scriptures, especially from the Upanishads, the Bhagavad Gita and the Zoroastrian Classics. He was thus influenced by the Indian philosophy; the trenchantly told in the mentioned texts helped him arrive at a unified sense of life. By drawing upon the sources of the ancient wisdom of the East, Eliot tells in telling terms that modern desolation and self-damnation can only be fought at the level of deeper subjectivity. This paper attempts to discover Eliot’s response to the amazing corners of the oriental wisdom which does not seem to have received a significant attention by the researchers. Key words: Eliot; Wisdom; Religion; Culture; Buddhism; Hinduism

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.910

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.060
GPT teacher head0.438
Teacher spread0.378 · 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 designQualitative
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
Published2011
Admission routes1
Has abstractyes

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