Reliability and Validity of a Scale-based Assessment for Translation Tests
Bibliographic record
Abstract
Are assessment tools for machine-generated translations applicable to human translations? To address this question, the present study compares two assessments used in translation tests: the first is the error-analysis-based method applied by most schools and institutions, the other a scale-based method proposed by Liu, Chang et al. (2005). They have adapted Carroll’s scales developed for quality assessment of machine-generated translations. In the present study, twelve graders were invited to re-grade the test papers in Liu, Chang et al. (2005)’s experiment by different methods. Based on the results and graders’ feedback, a number of modifications of the measuring procedure as well as the scales were provided. The study showed that the scale method mostly used to assess machine-generated translations is also a reliable and valid tool to assess human translations. The measurement was accepted by the Ministry of Education in Taiwan and applied in the 2007 public translation proficiency test.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".