Bibliographic record
Abstract
Abstract This paper offers a cognitive semantic analysis of 185 nominal-nominal compounds that are used to express Japanese traditional colors (e.g., budoo-nezumi [grape-rat] ‘plum purple’). It explores the types of nominals adopted into compounds, the components’ semantic relations, and the types of metonymy involved in the meaning construction. The most frequently found semantic relations of the two components of the compounds are: (i) color of the ‘right’ blended with color of the ‘left’ , where both components are construed metonymically via whole for the part (e.g., budoo-nezumi [grape-rat] ‘plum purple’ is a blend of two colors: grey, expressed by nezumi ‘rat’ (whole), standing for the animal’s hair color (part), and dark purple, expressed by budoo ‘grape’ (whole), standing for the fruit’s skin color (part)); and (ii) color of the ‘left’ , expressed by the X-iro [X-color] compound (e.g., kohaku-iro [amber-color] ‘amber’). While both components in the X-iro compounds are typically used literally, overall, 65% of the 185 compounds involve metonymy ( whole for the part, action for result , among others), suggesting the important role played by metonymy in meaning construction of the compounds expressing Japanese traditional colors.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".