Metonymic-Based Metaphor—A Case Study on the Cognitive Interpretation of “Heart” in English and Chinese
Bibliographic record
Abstract
<p>English is particularly rich in both metonymic and metaphorical expressions making use of the concept <em>heart</em> to speak of emotional issues (Niemeier, 2000). It is not difficult to find a large number of Chinese linguistic expressions in terms of “心 (<em>xin</em>) (<em>heart</em>)” to refer to emotion or other concepts. In the present study, under the categorization of <em>heart</em> by Niemeier (2000), we took some examples of <em>heart</em> in Chinese and gave a comparison between the metonymy-based conceptual metaphors in these two languages. This study found some positive evidence for the metonymic base for metaphors. In addition, there are some different interpretations of Chinese <em>heart</em> expressions due to the specific culture background.</p>
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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".