Cognitive Factors Related to Metaphor Goodness in Poetic and Non-literary Metaphor
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Bibliographic record
Abstract
In this paper we examine the effect of two cognitive variables, Semantic Neighborhood Density and Interpretive Diversity, in first, distinguishing between literary (poetic) and nonliterary metaphor, and second, in determining what makes for a good metaphor. Analyses of items taken from a widely used set ofmetaphor norms indicated that while literary and nonliterary metaphor did not differ in many ways, the poetic items tended to 1) contain concepts that came from a more dense semantic space, 2) contain topic and vehicles that came from equally dense semantic space, 3) suggest a greater number of possible interpretations as the topic and vehicle became more semantically dissimilar, and 4) evoke more emergent interpretations (i.e., less likely to be a characteristic of the topic or vehicle when considered separately). In addition, we found one way that the two variables were related to metaphor goodness: better metaphors were those with vehicles that came from increasingly less dense semantic space. This correlation was only reliable for literary, poetic items, presumably because these items were taken from a richer semantic environment suggesting many more alternative possibilities.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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 it