Translation Evaluation: The Suitability of the Argument Macrostructure Model for the Assessment of Translated Texts across Different Fields
Bibliographic record
Abstract
Even though there exists an undeniable need for an acceptable translation among translators, translation scholars and translation teachers, the question of acceptability and the criteria against which this acceptability can be determined are still controversial. There is a lack of generally agreed criteria against which translation can possibly be evaluated, despite the fact that international as well as local standards of translation are clearly witnessed. In spite of the fact that some scholars suggest certain parameters that can be utilized for the purpose of translation evaluation, such as quality of TL, accuracy, register, appearance of TT, situationality, and so on, there seems to be no parameter on which evaluators may rely to arrive at an overall quality assessment for the TT. The present paper argues that the argument macrostructure model should be utilised as a translation evaluation parameter to determine the translation quality. This model should comprise several standards and grades peculiar to different translation contexts and situations, so that it can successfully be applied in the case of assessing translation materials within both academic as well as professional settings.
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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 | 0.042 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".