Cognitive Factors Related to Metaphor Goodness in Poetic and Non-literary Metaphor
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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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 | 0.003 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".