Strategy to Block Interference from the Source Language (cognate signifiants) in Korean-Chinese Interpretation
Bibliographic record
Abstract
Although Korean and Chinese are not from the same family of languages, they have the common denominator of cognate signifiant that is, both languages can be written with the same methods of expression. In this case cognate signifiant means that both Korean and Chinese can be expressed in Chinese characters. There are many similarities in the visual and acoustic images of the two languages and for this reason cognate signifiant persistently intervenes in interpretation of one to the other. Therefore, the purpose of this study is to highlight through the analysis of case studies how cognate signifiant causes interference by hindering the extraction of meaning in Korean-Chinese interpretation, and to explore ways of increasing Korean-Chinese interpretation ability based on the results of such research. In order to approach this issue, recorded examples of incorrect interpretation resulting from interference caused by cognate signifiant will be analyzed from the perspective of interpretation studies, which places importance on the conveyance of meaning for the purpose of achieving communication. Based on the results of such research, strategies to effectively block interference resulting from cognate signifiant will be established.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".