Bibliographic record
Abstract
Class inclusion theory asserts that one cannot reverse the topic and vehicle of a metaphor and produce a new, meaningful metaphor that is based on the same interpretive ground. In 2 experiments we test that claim. In Study 1 we replicate the procedures employed by Glucksberg, McGlone, & Manfredi (1997) that provided support for the assertion. However we now add experimental conditions in which the target metaphors, either with the topic and vehicle in its canonical order or reversed, are placed in discourse contexts that provide support for a meaningful interpretation based on the same ground. In contrast to the prediction of class inclusion theory, fully 72% of the cases the reversed metaphors were rated as interpretable and interpretation was based on the same ground used in interpreting the metaphors in their canonical order. In Study 2, the online processing of the metaphors in context are examined in a word-by-word reading task. We find that canonical and reversed order metaphors were read at the same rate throughout and both sets exhibited the same reading patterns: increased reading time of the noun-phrase (NP) that contains the metaphoric vehicle and of the first word in the text that follows the metaphor. We take these data to indicate that nonreversibility cannot be taken as a necessary condition of metaphor.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.004 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".