Bibliographic record
Abstract
Views on the role of metaphor in language have varied ever since Aristotle discovered it and described its features. Traditional accounts of metaphor have seen it as a form of comparison or substitution for literal language, or else a “deviant” form of semantics. This situation changed in the twentieth century, starting with the work of I. A. Richards and the early gestalt psychologists, who put forward arguments and evidence that led, by the later part of the century, to the view that metaphor was more than a digression from literal language; rather, it was a trace of how meaning and concepts are formed. The major models that are derived from this perspective are generally discussed under the rubrics of “interaction,” “projection,” and “blending.” Each model can be described as a “unidirectional” one, since it posits essentially that metaphor is the result of enlisting concrete vehicles in order to shed light on (and even construct) abstract topics. By and large, these models have not entertained the possibility that metaphor is actually a “bidirectional” process, not only enlisting concrete vehicles to guide abstract conceptualization but also the reverse: abstract topics allow us to understand the vehicles. In other words, the parts of a metaphor implicate each other in tandem. This article will argue for a bidirectional model of metaphor. Such a model has implications for both linguistics and psychology — mainly, that the traditional view of metaphor as a process that makes abstractions understandable through vehicles or source domains may be moot, even though it might have practical functions and validity in specific instances of cognition.
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.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".