On Reversing the Topics and Vehicles of Metaphor
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Class inclusion theory asserts that one cannot reverse the topic and vehicle of a meta-phor 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 meta-phors, 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 pat-terns: 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. In a metaphor, the meaning of one concept (the vehicle or source) is used to inform or create the meaning attributed to a second concept (the topic or target). As such, meaning could be transferred to the topic that might not be considered when the topic is presented by itself. Thus in the nominal metaphor, my job is a jail, the vehi-
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it