Aptness Predicts Metaphor Preference in the Lab and on the Internet
Bibliographic record
Abstract
Experimental studies have suggested that variables such as aptness (Chiappe & Kennedy, 2001) or conventionality (Gentner & Bowdle, 2008) are predictors of people’s preference for expressing a particular topic–vehicle pair (e.g., “time–money”) as either a metaphor (“TIME IS MONEY”) or a simile (“TIME IS LIKE MONEY”). In the present study, we investigated if such variables would also be predictive within a more naturalistic context, where other variables, such as the intention to include an explanation (Roncero, Kennedy, & Smyth, 2006), may also influence people’s decision. Specifically, we investigated the production of metaphor and simile expressions on the Internet via the Google search engine and checked for accompanying explanations, as well as the properties they expressed, to examine whether ratings such as aptness, conventionality, as well as participants’ own stated preference or the intention to produce an explanation, would predict which topic–vehicle pairs appeared more often as metaphors. We found that participants’ stated preference predicted metaphor dominance on the Internet, and that apt topic–vehicles occurred more often as metaphors. The explanations collected, however, occurred 82% of the time after similes, and familiar expressions were the most explained. Finally, comparing the properties expressed in these explanations to normed property lists, we found that simile explanations typically expressed a novel conception of the topic–vehicle relationship. Therefore, we found that Internet posters use metaphors to convey an apt relationship, as found in previous laboratory studies, but prefer using a simile frame when they want to express a relationship that readers will find novel.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".