A Comparative Study on Basic Emotion Conceptual Metaphors in English and Persian Literary Texts
Bibliographic record
Abstract
Metaphor becomes the subject of interest for many researchers in recent decades. The main purpose of the present study was to investigate the universality of emotion metaphorical conceptualization and the dominant pattern in English and Persian based on Kovecses’s (2003) model for Linguistic expression of Metaphor. The emotions under study were happiness, anger, sadness, fear, and love. Lakoff and Johnson’s (1980) Conceptual Metaphor Theory was adopted as a model for the purpose of comparison. To do so, 782 emotive metaphorical expressions were compiled from different literary works and related articles on the field and Dictionaries in both languages. The study was conducted through two main phases of categorization and comparison. First expressions were categorized under their general and specific target and source domains. At the second phase, in each category, metaphorical expressions were compared with based on their conceptual metaphor and literal meaning. At this phase, three patterns of totally the same, partially the same, and totally different were identified. Also the results of Chi-Square applied to these three patterns demonstrate that anger ( = 108.85, P<0/000) was the most universal emotion, whereas sadness ( = 31.40, P< 0/000) was the least universal emotion during this study. In addition, the dominant pattern at the end of analysis was the pattern of totally the same.
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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 | 0.000 | 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".