Familiarity, aptness, concreteness, metaphoricity, and structure norms for 300 two-word metaphors in context and in isolation
Bibliographic record
Abstract
Familiarity, aptness, concreteness, metaphoricity, and structural norms for 300 two-word English metaphorical expressions (e.g., broken heart, early bird), presented in sentence context and in isolation, were obtained from 164 participants. Familiarity was conceived as the extent to which participants had previously heard or read that expression. Aptness was conceived as the extent to which the vehicle captured important features of the topic. Concreteness was conceived as the extent to which the meaning conveyed by the vehicle could be perceived through senses or actions. Metaphoricity was conceived as the extent to which the expression was perceived as figuratively rather than literally true. Metaphor constituent structure was conceived as a graded measure indicating whether the metaphorical content is carried by the first word, the second word, or distributed across both words. In addition to these variables, which are known to play a key role in metaphor comprehension, we provide frequency scores for the whole expression as well as for each constituent separately from the Corpus of Contemporary American English (COCA) database. Cumulative logistic regression was used to examine the effects of context and vehicle position on our ordinal ratings, as well as to assess whether familiarity and concreteness predicted metaphoricity. This set of norms, the first of its kind, serve as materials for research employing a variety of computational, behavioral, and neuroimaging methods aiming to tap the nature of metaphor comprehension and semantic composition.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".