Intertextuality Between T. S Eliot and Al Sayyab’s Poetry
Bibliographic record
Abstract
The purpose of this study is to shed light on ‘intertextuality’ as a cross cultural technique between modern Arabic and English poetry with reference to T. S Eliot and Al Sayyab. It aims to uncover the intertextual aspects of ‘allusion’, ‘symbols and myths’, ‘irony’, ‘the objective equivalent’, ‘conceptual metaphor’ and ‘impersonality’ between Eliot and Al Sayyab and the impact of Eliot’s thoughts, themes, expressions and style on Al Sayyab’s. However, the study reveals that the strategy of intertextuality takes a one-way direction, i.e., from Eliot to Al Sayyab, and Eliot’s fingerprints are quite manifest in Al Sayyab’s poetry. Moreover, although some of Eliot’s key expressions, ideas, symbols, myths and themes are borrowed by Al Sayyab, he could professionally use intertextuality and maintain his illustrious style.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".