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Record W2399419509 · doi:10.7202/1036141ar

Directionality in Signed Language Interpreting

2016· article· en· W2399419509 on OpenAlexvenueno aff
Jihong Wang, Jemina Napier

Bibliographic record

VenueMeta Journal des traducteurs · 2016
Typearticle
Languageen
FieldHealth Professions
TopicInterpreting and Communication in Healthcare
Canadian institutionsnot available
FundersMacquarie University
KeywordsSign languageInterpreterLinguisticsFirst languageLanguage proficiencyComputer sciencePsychologyNatural language processing

Abstract

fetched live from OpenAlex

This mixed methods study investigated the effects of directionality (language direction) and age of signed language acquisition on the simultaneous interpreting performance of professional English/Auslan (Australian Sign Language) interpreters, who comprised native signers and non-native signers. Each participant interpreted presentations simultaneously from English into Auslan, and vice versa, with each task followed by a brief semi-structured interview. Unlike a similar study, results reveal no significant differences between the native signers’ English-to-Auslan simultaneous interpreting performance and their Auslan-to-English simultaneous interpreting performance, suggesting that balanced bilingual interpreters are free from the rule of directionality. Although this finding held true for the non-native signers, results indicate a need for the non-native signers to continue to enhance their signed language (L2) competence. Furthermore, although the native signers were similar to the non-native signers in overall simultaneous interpreting performance in each language direction, the native signers were significantly superior to the non-native signers in both the target text features and delivery features of English-to-Auslan simultaneous interpreting performance. These findings also suggest that the non-native signers need to further improve their signed language (L2) proficiency. Nevertheless, an analysis of the qualitative interview data reveals that the professional interpreters perceived distinct challenges that were unique to each language direction.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.337
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.087
GPT teacher head0.430
Teacher spread0.343 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2016
Admission routes1
Has abstractyes

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