Technologies, Discourse Analysis, and the Spoken Word: The MRC Approach: An Empirical Approach to Interpreter Performance Evaluation and Pedagogy
Bibliographic record
Abstract
Given the evanescent quality of the spoken word, interpreters tend to be evaluated, trained, and selected on the basis of unproven theories and preconceptions about the cognitive processes and areas of difficulty associated with their work. A gap persists between theoretical work and empirical evidence of the processes proposed by such studies. Recent developments in technology are now being applied to interpreter performance evaluation, shedding light on aspects of interpreter performance that have previously resisted systematic analysis. It is now possible to examine large volumes of language in use, in both audio and textual realms. This paper presents the MRC model for analysis of interpreter performance and a study conducted using that method for the purpose of identifying interpreter training needs. Theoretical background, the MRC model, and the study outcomes and pedagogical implications are presented.
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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 | 0.043 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.007 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".