Bibliographic record
Abstract
Abstract How metaphors are comprehended and their pragmatic effects have long been of interest ( Colston, 2019 ; Gibbs, 2017 ; Gibbs & Colston, 2012 ). Attending to the varied constructions where metaphors appear has also advanced our understanding ( Athanasiadou, 2017 ). How metaphors in extra-linguistic mediums has been of particular interest of late, as have the import of embodied simulations ( Bergen, 2012 ; Cienki & Muller, 2008 ; Forceville & Urios-Aparisi, 2009 ). But these explorations outside of language and inside the body have left some bits relatively unattended. Metaphors and their host constructions can be embedded into an array of language genres. These also bring their own idiosyncratic influences on metaphor cognition. Moreover, the target domain content that metaphors invoke adds to this complex mixture. And metaphors can be layered within metaphors. This paper presents a tour of just some of the immense fractalesque complexity invoked when we look at the full richness of 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.022 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".