A CDA Approach to Translation Quality Assessment: A Case Study of Lost Symbol
Bibliographic record
Abstract
Translation Quality Assessment (TQA) is the pivotal point of this study with a focus on discourse and mainly Critical Discourse Analysis (CDA) in order to find out how far it is possible to transfer discourse as a culture based concept from source community into the target community. This transfer is regarded as a yardstick to assess the translator’s overall accomplishment and the quality of the translation as well. For the same purpose Van Dijk CDA Framework (2004) has been used to assess the Farsi translation of Dan Brown’s novel Lost Symbol which has been done by Hosein Shahrabi. A thorough comparison between the selected data from the source text and the related translations in term of the discursive/pragmatic strategies and the results of Chi-Square Tests showed that the transfer of discourse from source into the target is possible with the least of deviations and the fact that discourse is a concept rooted in the culture of the people and parties involved in the act of translation, would not hinder the efforts put by the translators to create the same discourse in the target society for the end readers. The safe transfer of discourse from source into the target is considered to be the main yardstick for the assessment of translation quality under this study.
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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 | 0.022 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.016 | 0.010 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".