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Record W2089197120 · doi:10.7202/016737ar

Strategies for Abating Intercultural Noise in Interpreting

2007· article· en· W2089197120 on OpenAlexvenueno aff
Jing Chen

Bibliographic record

VenueMeta Journal des traducteurs · 2007
Typearticle
Languageen
FieldHealth Professions
TopicInterpreting and Communication in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsIntercultural communicationContext (archaeology)InterpreterPsychologyIntercultural relationsProcess (computing)Noise (video)Task (project management)Situational ethicsQuality (philosophy)Cognitive psychologyLinguisticsComputer scienceSocial psychologyCommunicationEngineeringEpistemologyGeographyArtificial intelligence

Abstract

fetched live from OpenAlex

The nature of interpreting and the task it fulfills decide that it is an intercultural communicative act. There are two types of intercultural noise disturbing the communication process, that originating from the information sent by the source-language speaker, and that coming from the social, cultural and situational context of the communication process. Intercultural noise impedes the interpreting process and debases the quality of interpreting. If the ideal function of an interpreter is to ensure smooth communication between the primary parties, then his role is to remedy any potential intercultural noise in the channel. This paper then aims to formulate concrete intercultural noise-reducing strategies, which include long-term strategies, pre-interpreting strategies and during-interpreting strategies.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.029
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0030.006
Scholarly communication0.0050.005
Open science0.0020.005
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.122
GPT teacher head0.451
Teacher spread0.329 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations4
Published2007
Admission routes1
Has abstractyes

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