When the Focus of the Text is Blurred: A Textlinguistic Approach for Analyzing Student Interpreters' Errors
Bibliographic record
Abstract
This study aims to investigate the effect of missing the pragmatic impact of two textual components, viz., conjunctives and key lexical items, on the typological focus of the source text in the process of simultaneous interpreting from Arabic into English. The source text assumes a hortative function which calls into the recipient's active socio-historical memory, events and experiences comparable to those addressed in the text. The investigation is based on the assumption that in a hortative text conjunctives and lexical items play a significant role in displaying the pragmatics of the communicative event. The study investigates how five Arabic conjunctives and four emotively-loaded lexical items in the text were rendered in English by ten MA. (Translation) students. This research has shown that the conjunctives were inappropriately rendered by most of the student interpreters, and that such renderings distorted the intended argument of the text. Results also reveals that the interpretations provided of the four key lexical items stripped them of their emotive charge, thus neutralizing the argument of the text. The study concludes with suggestions concerning the methods of teaching interpreting and the content of the interpreting course at Yarmouk University. The suggestions are based on the implications derived from the analysis of both the source text and the students' renderings of the conjunctives and the lexical items.
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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 | 0.005 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".