Analysis of ‘Heart’ Metaphors in the Translation of To The Lighthouse: A Cognitive-Inspired Approach
Bibliographic record
Abstract
Metaphors as a matter of thought can differ across languages and cultures depending on life experience, cultural background and individual differences. Emotion-related metaphors are widely used specifically in literary texts for conveying certain effects. Translating emotion-related metaphors can be a hard task, as problems arise due to differences between two conceptual systems. Several researchers have highlighted the importance of Conceptual Metaphor Theory (CMT) for understanding and analyzing metaphors. This study aims to analyze emotion-related metaphors, particularly the ‘heart’ metaphors extracted from the novel To The Lighthouse and their translation into Arabic. In the analysis, the researchers identified the underlying conceptual metaphor of each metaphorical expression and looked at how it was expressed at the linguistic level. The conceptual metaphors of the source text (ST) and the target text (TT) were then categorized considering the Cognitive Translation Hypothesis (CTH). The findings indicate that ‘heart’ metaphors were used similarly at the conceptual level in both languages, but in some cases, they differed slightly at the linguistic level. This confirms the basic premise that some emotion-related concepts are universal and deeply rooted in our thought and cognition. The translator used the strategy of addition mostly to produce a plausible equivalent. Moreover, it was found that ‘heart’ occurred more frequently in the Arabic translation of the novel in which metaphorization was used to express emotions in the ST even if ‘heart’ was not used in the expression.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".