Embedded Metaphor & Subsentential Pragmatics: Revisiting the Scope Argument
Bibliographic record
Abstract
The so-called ‘scope argument’ challenges Gricean theories of metaphor by claiming that metaphorical readings are directly expressed. That is, because metaphorical readings survive under the scope of logical and intensional operators, they figure in what is said/explicitly communicated. In this article, I resist that conclusion. I show that other putative implicatures pass the scope test to motivate the idea that _at least _some implicated content arises within embedded contexts while resisting the claim that such content is what is said. To deal with such content, I argue that local, pragmatically inferred content is truth-conditionally relevant without thereby being a part of what is said. This move carries important consequences for how to draw the boundary between semantics and pragmatics. It raises two additional challenges for a theory of metaphor: the calculation and compositionproblem. I address these challenges by sketching a subsentential Gricean model whereby embedded metaphor is treated as a local implicature triggered by pressures on Gricean maxims, compositionally integrated by type, whose meaning is predictably indeterminate and defeasible, unlike said content. Building on work on embedded implicatures, this model preserves Grice’s cooperative architecture while explaining metaphor’s truth-conditional ‘effects’. The result is a lean semantics with a principled account of embedded metaphorical meaning.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.004 | 0.016 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".