Analytic rubric scoring versus comparative judgment: a comparison of two approaches to assessing spoken-language interpreting
Bibliographic record
Abstract
In this article, we report on an empirical study conducted to evaluate the utility of analytic rubric scoring (ARS) vis-à-vis comparative judgment (CJ) as two approaches to assessing spoken-language interpreting. The primary motivation behind the study is that the potential advantages of CJ may make it a promising alternative to ARS. When conducting CJ on interpreting, judges need to compare two renditions and decide which one is of higher quality. Such binary decisions are then modeled statistically to produce a scaled rank order of the renditions from “worst” to “best.” We set up an experiment in which two groups of raters/judges of varying scoring expertise applied both CJ and ARS to assess 40 samples of English-Chinese consecutive interpreting. Our analysis of quantitative data suggests that overall ARS outperformed CJ in terms of validity, reliability, practicality and acceptability. Qualitative questionnaire data helped us obtain insights into the judges’/raters’ perceived advantages and disadvantages of CJ and ARS. Based on the findings, we tried to account for CJ’s underperformance vis-à-vis ARS, focusing on the specificities of interpreting assessment. We also propose potential avenues for future research to improve our understanding of interpreting assessment.
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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 | 0.156 | 0.374 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".