Realizations of Conceptual Metaphors of ANGER in Arabic, Russian and English: A Contrastive Corpus-Based Approach
Bibliographic record
Abstract
Conceptual metaphors are often analyzed out of context. Nevertheless, the crucial role of context is evident as metaphors do not only transmit specific entailment of particular concepts, but they also reflect cultural and social characteristics. At the same time, one cannot deny that conceptualization is involved in the interpretation of various cultural models and conceptual metaphors. The purpose of the current research was to analyze conceptual metaphors of ANGER in Arabic, Russian and English. The current study employed a contrastive corpus-based approach to compare and contrast the conceptual metaphors of ANGER in the aforementioned languages. The outcomes of this research study contributed immensely to the existing literature on conceptual metaphors analysis as there are almost no previous researches done in the field comparing three languages belonging to different language groups. The study found that the Arabic language demonstrated the highest tendency towards conceptual metaphors formation out of the three languages. The study confirmed that cultural context played a significant role in the formation of conceptual metaphors, and it also proved that due to different cultural environments, some metaphors are unique by nature and can be present only in a particular language. It can be concluded that conceptual metaphors of ANGER are not a universal concept, and cultural norms and values make this concept non-identical in the aforementioned languages.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".