Bibliographic record
Abstract
Using Figurative Language presents results from a multidisciplinary decades-long study of figurative language that addresses the question, 'Why don't people just say what they mean?' This research empirically investigates goals speakers or writers have when speaking (writing) figuratively, and concomitantly, meaning effects wrought by figurative language usage. These 'pragmatic effects' arise from many kinds of figurative language including metaphors (e.g. 'This computer is a dinosaur'), verbal irony (e.g. 'Nice place you got here'), idioms (e.g. 'Bite the bullet'), proverbs (e.g. 'Don't put all your eggs in one basket') and others. Reviewed studies explore mechanisms - linguistic, psychological, social and others - underlying pragmatic effects, some traced to basic processes embedded in human sensory, perceptual, embodied, cognitive, social and schematic functioning. The book should interest readers, researchers and scholars in fields beyond psychology, linguistics and philosophy that share interests in figurative language - including language studies, communication, literary criticism, neuroscience, semiotics, rhetoric and anthropology.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".