Bibliographic record
Abstract
This article proposes three constraints on the complex polysemy patterns of onomatopoeia. Onomatopoeic forms for thirty types of sounds were collected from Japanese, Korean, Mandarin Chinese, and English, and examined in terms of their semantic extensibility and extension types. It was found that 1) Chinese onomatopoeia is generally resistant to semantic extension, 2) onomatopoeic forms for voice in Japanese and Korean are less likely to be polysemous than those for noise, many of which show metonymical extension, and 3) many onomatopoeic verbs in English have metaphorical meanings. These three patterns can be accounted for by generalizations that associate high semantic extensibility with referential specificity, event-structural complexity, and syntactic coreness, respectively. Each of these generalizations finds some independent support, and is compatible with or complementary to a frame-semantic approach to polysemous onomatopoeic forms, which has been taken to discuss existing, rather than non-existing, cases in each language. This study is, thus, an attempt to locate sound symbolism research in the center of cognitive studies of language.
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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".