The Power of Non-Literal Language: An Enquiry Into the Meaning of Metaphor
Bibliographic record
Abstract
In this thesis, we will survey and critically analyze the philosophical discourse on metaphor as it has been traditionally defined by three main theories: the pseudo-semantic theory of Max Black, the non-cognitive theory of Donald Davidson, and the pragmatic theory of John Searle.With this critical analysis, we will see how these three philosophers have contributed to our understanding of what metaphors are, what metaphors mean, where the "power" of metaphors lie, and how we can restrict our possible interpretations of a metaphor.From here, we will see how the leading theory in the philosophy of metaphor, Relevance Theory, has attempted to accommodate for the virtues and vices of these three theories and some of the resistance that this theory has been met with.
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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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.066 |
| Scholarly communication | 0.008 | 0.031 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".