On the Initial Exploration of Technical Word in Middle Chinese
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Bibliographic record
Abstract
Technical words spread into the lexical system of Middle Chinese gradually in due order. The diffusions can be divided into four stages: Firstly, technical words begin to spread into documents; Secondly, they spread into documents related to the former documents; Thirdly, they spread into common documents; Finally, they end the spread. The main characteristics of word spread correspond to the free speech creation, the elimination and selection of technical words, the related documents played the role of a bridge during the diffusions, and the division of the technical words and the faintness of the technical word flavour. Key words: technical words, diffusing of words, Middle Chinese Resume La diffusion de la terminologie dans le systeme lexique du chinois antique moyen est un processus progressif, qui peut etre divise en quatre etapes : diffusion initiale dans le document ecrit, diffusion a courte distance dans le document correlatif du metier, diffusion totale dans le document general, l’epilogue de la diffusion dans le document ecrit. Les caracteristiques respectives des quatre etapes : la creation hautement libre de la langue ; la normalisation engend l’elimination et l’election a grande echelle ; le document correlatif du metier sert de pont pour la diffusion du terme ; la diffferenciation du vocabulaire apres de maints selections par la langue ecrite anisi que l’effacement de la couleur du metier.. Mots-cles: la termilologie, la diffusion du vocabulaire, le chinois antique moyen 摘 要 專門詞語向中古漢語辭彙系統的擴散是一個循序漸進的過程。劃分為四個階段：向書面文獻的初始擴散、向行業關聯文獻的近距離擴散、向普通文獻的全面擴散、向書面文獻擴散的尾聲。各階段主要特點是：高度自由化的言語創新；規範整理導致大規模汰選；行業關聯文獻成為詞語擴散的橋樑；經書面語多次遴選後的詞語分化以及行業色彩的淡化。 關鍵詞：專門詞語；詞語擴散；中古漢語
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it