Proverbs Translation for Intercultural Interaction: A Comparative Study between Arabic and English Using Artificial Intelligence
Bibliographic record
Abstract
Proverbs are a source of wisdom and morals that have been passed on from one generation to the next throughout history. Through intercultural interaction, it appears that some proverbs were either translated or have equivalents in different languages and cultures. This study used artificial intelligence, specifically machine learning techniques, to examine five equivalent proverbs in Arabic and English. Emphasis was placed on the occurrence of equivalent proverbs in different contexts through a collection of actual language use. A topic modeling algorithm was applied to a dataset for each proverb to explore the latent topics/themes that construct its meaning. The results revealed subtle differences between equivalent proverbs in Arabic and English, mainly due to religious, cultural, and social factors. Translators are thus encouraged to be aware of nuances in meanings, develop intercultural pragmatic knowledge to communicate the intended meaning and avoid misunderstandings.
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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 | 0.001 |
| 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".