Bibliographic record
Abstract
Among different types of metaphor, a certain kind of interesting example of physical-physical polysemy is so-called synaesthetic metaphors. In a synaesthetic metaphor, a certain perceptual mode is initially specified (or may be assumed), but the imagery is linguistically related in terms belonging to one or more differing perceptual modes. “Bright sounds” and “warm orange” make perfect sense to us. This understanding uniform commonly relies on perceptual equivalence, which makes cross-modal association possible. Tabulations of the frequency of types of synaesthesia and synaesthetic metaphors in English reveal the inner trend of how the perceptual similarities is realised in English language use. Key words: synaesthesia, synaesthetic metaphor, perceptual similarity Resume: De nos jours, le mot « belle », appellation des femmes, est tres populaire en Chine. Le mot « belle », n’etant plus reserve aux femmes jeunes et jolies, devient une appellation polie pour toutes les femmes. Le present article, en combinant la methode quantitative et la methode qualitative de recherche, examine profondement l’utilisation de ce mot dans 4 villes chinoises et sa fonction pragmatique dans la communication. L’auteur etudie aussi des informations sur le locuteur et l’interlocuteur, telles que le sexe, l’âge, leur relation, etc. Mots-cles: mot d’appellation, belle, fonction pragmatique 摘要： 在眾多不同類型的隱喻表達中，有一種有趣的物質性對物質性的多義詞現象叫做通感隱喻，又稱移覺，即一種感官模式可以在語言上以另一種感官模式得以體現。顏色似乎會有溫度，聲音似乎會有形象，冷暖似乎會有重量，氣味似乎會有鋒芒。我們完全可以理解“Bright sounds”（嘹亮的聲音） 和“warm orange”(暖洋洋的橙色)所表達的意思。人們解讀通感修辭方面驚人的一致性取決於感知上的對等，同時這種感知相似性使得人們能夠進行交叉感知聯想。關於英文通感隱喻使用頻率和類型的統計揭示了該感知相似性體現在英語應用中的內在趨勢。 關鍵詞：通感；通感隱喻；感知相似性
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".