Bibliographic record
Abstract
Some theorists argue that Grice's account of metaphor is intended as a rational reconstruction of a more general inferential process of linguistic communication (i.e., conversational implicature). However, there is a multi-source trend which treats Grice's remarks on metaphor as unabashedly psychological. The psychologized version of Grice's view runs in serial: compute what is said; reject what is said as contextually inappropriate; run pragmatic processing to recover contextually appropriate meaning. Citing data from reaction time studies, critics reject Grice's project as psychologically implausible. The alternative model does not rely on serial processing or input from what is said (i.e., literal meaning). I argue the serial processing model and its criticisms turn on a misunderstanding of Grice's account. My aim is not to defend Grice's account of metaphorper se, but to reinterpret auxiliary hypotheses attributed to him. I motivate two points in relation to my reinterpretation. The first point concerns the relationship between competence and performance-based models. To the second point: Several of the revised hypotheses make predictions that are largely consistent with psycho and neurolinguistic data.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".