Mechanisms involved in the formation of metaphorical classes within the framework of the class-inclusion model of metaphor comprehension
Bibliographic record
Abstract
According to Glucksberg’s class-inclusion model of metaphor comprehension, metaphors are understood by the inclusion of the topic X into a metaphorical class of the vehicle Y. But what is the cognitive mechanisms through which X is included in the metaphorical class of Y? Drawing on previous literature on the roles of semantic features, metonymy, and relations in metaphor processing, this article presents a new proposal according to which every metaphorical class is defined by one of three categories of a concept’s characteristics: semantic features, metonymic aspects, or relational aspects. Each category may consist of a large set of such characteristics. One characteristic (or at most several characteristics) usually defines the metaphorical class of Y. Additionally, it is proposed that the metaphorical class is created by the suppression of metaphorically-irrelevant characteristics, consistent with ideas from Relevance Theory. The result of this process is a metaphorical class which has a higher degree of abstractness compared to the literal class of Y. Finally, it is proposed that the three categories of characteristics may be in interaction with each other. Therefore, in some cases, two or even three categories of characteristics may be involved in the formation of a metaphorical class, but one specific category plays the main role in the process.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.005 | 0.015 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".