A Case Study on the Influence of Ideology in Chinese-English Translation during the 2008 Beijing Olympic Games
Bibliographic record
Abstract
This study adopts translation model theory to analyze data from authorities responsible for Chinese-to-English translation of Beijing Olympics-related documents. By examining the translation process and investigating the supretextual and textual parameters influencing translators, the current paper aims at revealing the ideological influences on the translation practice. The adaptation translation strategies are employed in the translation of Olympic texts, but under one premise: the most conveyance of the ideological intentions in the source text. Key words: Translation model; Translation process; Ideology; Supretextual parameter; Textual parameterResume: Cette etude adopte la theorie de la traduction modele pour analyser des donnees des autorites responsables de la traduction chinoise-a-anglais de Pekin, des documents concernant les Jeux olympiques. En examinant le processus de traduction et examinant le supretextual et les parametres textuels influencant des traducteurs, le journal actuel vise a reveler les influences ideologiques sur la pratique de traduction. Les strategies de traduction d'adaptation sont employees dans la traduction de textes Olympiques, mais sous une premisse : la plupart de transport des intentions ideologiques dans le texte source.Mots-cles: Modele de traduction; Processus de traduction; Ideologie; Supretextual parametre; Parametre textuel
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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".