Perceptions of bilingual competence and preferred language direction in Auslan/English interpreters
Bibliographic record
Abstract
Given that the study of interpreting can be considered as an applied linguistic activity, this paper details a small-scale study which investigated 56 Australian Sign Language (Auslan)/ English interpreters’ perceptions of their bilingual status and compared these to their preferences for working into Auslan or English. The impetus for the study came from discussions with interpreter educators, researchers and practitioners in which it was asked ‘how bilingual’ an interpreter must be in order to interpret effectively. Interpreters are assumed to have a high level of proficiency in both their languages and traditionally interpret into their dominant language. An email survey that questioned interpreters’ perceptions about bilingualism in general, their own bilingualism and their preferred language direction was administered to accredited Auslan/ English interpreters in Australia. The results showed that for many of the interpreters, perceived bilingual status and preferred language direction when interpreting contravened established practice, preferring to interpret into their non-dominant language. The findings are discussed in relation to implications for the education and practice of signed and spoken language interpreters worldwide, and highlight the need for further study of the nexus between bilingualism and interpreting.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".