Eliot's Delving into the Oriental Wisdom: A Cross-Cultural Study
Bibliographic record
Abstract
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 In his poems, The Waste Land , and Four Quarters , and some of his plays, Eliot has withdrawn significant ideas from the Indian Scriptures, especially from the Upanishads, the Bhagavad Gita and the Zoroastrian Classics.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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".