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Pragmatic Failure in Consecutive Interpreting

2010· article· en· W2136451197 on OpenAlexvenueno aff
Wu Shang

Bibliographic record

VenueCanadian social science · 2010
Typearticle
Languageen
FieldArts and Humanities
TopicLanguage, Discourse, Communication Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsConscienceInterpretation (philosophy)InterpreterPsychologySociologyHumanitiesLinguisticsPhilosophyEpistemologyComputer science

Abstract

fetched live from OpenAlex

Consecutive interpreting is extensively used to help people speaking different languages overcome the barriers to cross-cultural communication. Pragmatic failure in consecutive interpreting can lead to misunderstanding or even offense. The paper analyzes possible causes of pragmatic failures in consecutive interpreting in terms of pragmalinguistic failures and sociopragmatic failures and the differences in languages, thoughts and cultures behind the pragmatic failures, with an intention to raise interpreters’ sensitivity to pragmatic force and cultural differences in cross-cultural communication.. Key words: pragmatic failure, pragmalinguistic failure, sociopragmatic failure, cultural difference, consecutive interpreting Resume: Dans l’activite communicative transculturelle qu’est l’interpretation consecutive, si l’enterprete ne prend pas conscience de l’echec pragmatique, il entrainera des malentendus dans la communication. L’article present, sur la base de la theorie de l’echec pragmatique, analyse les raisons de l’echec pragmatique avec des exemples de l’exercice d’interpretation dans l’intention d’elever le niveau de conscience de l’interprete sur l’echec pragmatique pour favoriser la communication. L’analsye s’effectue sous les angles de l’echec pragmalinguistique et de l’echec sociopragmatique. Mots-Cles: echec pragmatique, echec pragmalinguistique, echec sociopragmatique, difference culturelle, interpretation consecutive

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)0.000
metaresearch head score (Gemma)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.860
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.275
Teacher spread0.260 · 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 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

Citations1
Published2010
Admission routes1
Has abstractyes

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