Bibliographic record
Abstract
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
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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 | 0.019 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.033 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".