Bibliographic record
Abstract
Metaphors are undoubtedly contextual phenomena.We often creatively employ them to say one thing and mean another by exploiting the literal meaning of the words used.However, metaphors are not simply parasitic on literal meaning.Rather, they pull from ways interlocutors, readers, and audiences understand context, and they tap into our shared knowledge of the world and one another's mutual beliefs.In this thesis, I propose a pragmatic account of metaphor that treats metaphorical meaning as the result of recovering the speaker's intended meaning.I defend a broadly Gricean account of metaphorical content from more recent accounts that take metaphorical meaning to be directly, explicitly, and automatically interpreted by an audience.A related concern with my view is whether metaphors are distinctively unique from other forms of linguistic communication.According to recent philosophers of language, metaphors and their literal counterparts exploit the same basic cognitive processes.I disagree with this conclusion.I promote a position that treats metaphor on par with poetic imaginings that exploit characterizations (roughly, stereotypes) to novel ends.This makes my treatment of metaphor importantly different from contemporary treatments of it that reduce metaphor to 'literally loose' talk.I develop a framework of metaphoric communication that is based on Korta and Perry's (2011b) framework developed at length in "Critical Pragmatics".The theoretical utility offered by Korta and Perry's framework is made explicit throughout, but becomes especially important in Part II where I undertake to show how their framework allows me to subsume the nuances of communicating metaphorically within a broader theory of communication.gratitude to some of the individuals and institutions, without whose generosity and help this project wouldn't have been possible.First, and foremost, I want to thank my friend and supervisor, Eros Corazza.Your guidance, generosity, and sense of humour (and love of fine wine) have helped me become a philosopher, and a better man.We have spent countless hours discussing language, philosophy, and life.You have always kept me focused on my work; but not so focused as to mistake it for my life's project.Here is to many more years of friendship.I miei ringraziamenti.Second, I would like to thank Andrew Brook and Raj Singh for the support, care, and diligence that they have invested in my project, pro bono!Dr. Brook challenged me to hold myself and my writing to a higher standard.Dr. Singh has taught me that although there is a place for critique, 'you can't fight something with nothing'-and that in our line of work, to fight well often means you must 'go back to the basics'.Thank you to the Zoom group.Your collective efforts have helped me improve my thoughts about metaphor and beyond.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".