Bibliographic record
Abstract
Metaphors and similes have been treated as the same comparable types of figurative speech since Aristotle. In early theories, metaphors are interpreted as corresponding similes by paraphrasing. Based on this theoretical framework, some experimental studies interpret simile understanding as evidence for metaphor understanding. However, according to Relevance Theory, similes are interpreted as comparisons, whereas metaphors are interpreted as categorization statements. Therefore, only metaphor reveals metaphor interpretation. In other words, studies cannot use simile interpretation as evidence for metaphor interpretation ability in children. Simile should be easier to understand for children since they exclude an “ad hoc concept” construction as in metaphor understanding. This study seeks evidence showing children’s better performance in simile interpretation than that in metaphor interpretation, thus supporting Relevance Theory. For the Relevance Theory account, whether the experiments use similes or metaphors as testing materials is of considerable significance, whereas for comparison account, it is not. By review and re-interpretation of the empirical studies, we find that few early studies expose “real metaphor” understanding in children. Most experimental results indicate that simile interpretation is easier than metaphor interpretation for children. We consider comparison theory and the Relevance Theory as complementary strategies in metaphor interpretation.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".