The Contextualized Study on Metaphor: a Research on Metaphor in Chinese Classic Poems
Bibliographic record
Abstract
Studies on metaphor of decontextualized words or sentences bring some problems. By studying the metaphor of Chinese classic poems, this paper tries to study metaphor in the context of discourse. It argues that besides source discourse and target discourse, other aspects of context also play an important role in the metaphoric working process. Studying metaphor in contextualized discourse will extend the range of research on metaphor. Key words: metaphor, discourse, context, Chinese literary allusion, Chinese classic poems Resume: L’etude metaphorique des mots et des phrases isoles pourrait causer des problemes. A travers l’etude metaphorique des citations dans la poesie ancienne chinoise, l’article present tente d’effectuer l’etude metaphorique dans le texte, en indiquant que non seulement le texte source et le texte cible influent sur la metaphore, mais le contexte joue aussi un role important. L’auteur signale ainsi l’importance du contexte dans la recherche metaphorique. L’introduction du contexte ouvrira une nouvelle perspective pour l’etude de la metaphore. Mots-cles: metaphore, texte, contexte, citation, poesie ancienne chinoise 摘要：孤立的詞匯和句子層面的隱喻研究會帶來一些問題。本文通過中國古代詩詞中典故的隱喻研究，擬將隱喻研究放歸語篇，指出不僅源語篇和目標語篇對隱喻有至關重要的影響，而且語境各個方面，如上下文、情境和時代社會背景對隱喻的運作也起著重要的作用。從而揭示語境對隱喻研究的重要性。在語篇中聯係語境的研究將為隱喻研究提供新的視野。 關鍵詞：隱喻；語篇；語境；典故；中國古代詩詞
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".