Bibliographic record
Abstract
Metaphor has been the focus of cognitive linguistics, psycholinguistics, applied linguistics, corpus linguistics, and metaphor identification lays a solid foundation for metaphor research. Since Lakoff and Johnson (1980) proposed the Conceptual Metaphor Theory, much attention has been given to the conceptual and cognitive dimensions of metaphor, leaving linguistic dimension secondary. However, when MIP was introduced in 2007, which aims to identify metaphorically used lexical units in natural discourses, metaphor researchers have developed a systematic and reliable methodology for identifying linguistic metaphor instead of working with intuition and subjective criteria, which enables them to focus their research on different levels-linguistic forms, conceptual structure and cognitive processing. As MIP requires metaphor analysts to work through five steps, in which they depend heavily on dictionaries to determine lexical units and specify the basic and contextual senses, the use of dictionaries becomes the critical element in MIP. The Pagglejaz Group chose Macmillan English Dictionary for Advanced Learners a reference, while MIPVU, the elaborated version of MIP, used Longman Dictionary of Contemporary English and Oxford English Dictionary apart from Macmillan dictionary. The author, by demonstrating the use of different types of dictionaries in MIP, tries to show that together with learners’ dictionaries, historical dictionaries, collocation dictionaries and specialized dictionaries can also be used for cross reference to guarantee the reliability of linguistic metaphor identification.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".