A cognitive perspective on basic generic metaphors and their specific-level realizations
Bibliographic record
Abstract
By conducting an examination of the mapping process in metaphor comprehension, this article suggests that a set of superficially different metaphors can be considered to be isomorphic to an underlying generic metaphor. In other words, a set of seemingly different metaphors with different domains can be categorized under a single generic metaphor. The generic metaphor is in the general form of X is in some kind of semantic relationship with Y. When this generic metaphor is realized in specific-level forms, a number of metaphors are produced which are isomorphic to each other, although their domains could be completely different in appearance. In other words, there is a deep homogeneity among a set of concretely different metaphors. A generic metaphor can be seen as a semantic frame for all specific metaphors that are isomorphic to it. Since base and target domains of a given metaphor can be very different in terms of concrete features, the mapping of the base into the target must be mediated by the domain of its underlying generic metaphor.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 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 teacher head, 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".