A critical perspective on the translation quality assessments of five translators organizations: ATA, CTTIC, ITI, NAATI, and SATI
Bibliographic record
Abstract
The present paper discusses translation quality assessments by adopting a critical perspective on five translators organizations, which are intended to assess the quality of non-literary translations with a particular focus on their objectivity, validity, and inter-rater reliability. Within this framework, it aims to contribute to the related literature (1) by discussing the objectivity, validity, and inter-rater reliability of the quality assessment methods of five translators organizations, namely the American Translators Association (ATA), the South African Translators’ Institute (SATI), the Canadian Translators, Terminologists and Interpreters Council (CTTIC), the Institute of Translation and Interpreting (ITI in UK), and the National Accreditation Authority for Translators and Interpreters (NAATI in Australia), and (2) by making suggestions on quality assessment concerning non-literary translation in view of the findings hereof. The study qualitatively analyzes the content of the guides and rubrics provided on the websites of these five organizations and discusses the objectivity, validity, and inter-rater reliability of their assessments in consideration of seven parameters, i.e. “purpose of assessment”, “purpose of assigned translation”, “duration”, “source text”, “assessor”, “marking”, and “grading”. The findings showed that each organization suffers from varying degrees of objectivity, validity, and inter-rater reliability issues.
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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 | 0.216 | 0.310 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.014 | 0.035 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".