The Translatability of Metaphor in Eliot’s The Waste Land: A Comparative Approach
Bibliographic record
Abstract
There is an obvious gap in studying the translatability of metaphor in modern English poetry, particularly in Eliot’s The Waste Land. Furthermore, it is observed that most previous studies about metaphor are in and for English, and only few ones have tackled the translatability of metaphor into another language. However, the current study aims to explore this phenomenon in Eliot’s The Waste Land and three of its Arabic translations. All metaphors of The Waste Land and its three translations are identified, studied and classified into juxtaposed tables to facilitate the comparative process. Then, an assessment of each translation is made to be compared to the original text and the other translations. This comparison aims at identifying the translatability of metaphor in The Waste Land, the most and least used strategy and how the three translators have dealt with the original text. The study also shows that the three translators could translate most of Eliot’s metaphors into Arabic analogous metaphors; Lu’lu’ah uses this strategy the most and Raghib the least. Furthermore, the strategy of paraphrasing the metaphor is used more than the second one (11 cases). Finally, this study suggests three recommendations for further upcoming studies. The first one is: Conducting a comparative study on using metaphor in the spoken languages or dialects of two different societies (the Jordanian and British, for instance). The second is: Exploring this phenomenon in students’ everyday language; and the third is: Investigating the ability of English language students in rendering metaphor from English into Arabic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".