Essential characteristics of sign language interpreting students
Bibliographic record
Abstract
Interpreter educators who strive to recruit and retain students with the potential to succeed in their post-secondary programs want to be able to identify the complex personal and cognitive characteristics typical of these students. The present investigation expanded upon previous studies of second-language students and working interpreters by focusing on the characteristics of sign language interpreting students who had transitioned from language learning into interpretation coursework. An instrument was designed to evaluate student and faculty perceptions of the academic habits and skills, information processing, and personality traits most important for success in interpretation courses and those that needed the most development. A sample of sign language interpreting students and faculty (N = 1,357) was recruited in Austria, Canada, Great Britain and the United States, and participants selected online or paper versions of the instrument. Results indicated that achievement might be affected by factors such as interaction in the native sign language community, interaction with instructors, and repetition of language courses for enhancement. The responses of students and faculty were compared for agreement on the characteristics most likely to motivate students to complete rigorous interpreting programs and for characteristics that must be developed to improve confidence and performance.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".