Saudi Interpreters’ Adaptive Cognitive Strategic Behaviors in Consecutive Interpretation
Bibliographic record
Abstract
This study investigates the adaptive cognitive strategies used by Saudi interpreters in consecutive interpretation (CI) guided by Gile’s (2009) Effort Models. Using a structured questionnaire, insights were gathered from 102 Saudi interpreters, divided into undergraduate students and professional interpreters with varying levels of experience. The research focuses on how interpreters manage the selection of interpretation strategies across four phases of CI: listening, note-taking, note-decoding, and reformulating. Notable findings include the contrast between experienced professionals, who seldom rely on common sense, omitting, or paraphrasing, and trainees, who employ these strategies more frequently in response to unfamiliar topics. Professionals, regardless of experience, effectively use paraphrasing when faced with numerical complexities. When dealing with fast-speaking speakers, experienced professionals avoid omitting content, while trainees rely on this strategy. In conclusion, to manage concentration during long speeches, trainees prioritize attention to the source speech and use selective omission, while experienced professionals gravitate toward generalizing and summarizing approaches. This research highlights the influence of experience on strategy choice, aligning with Jääskeläinen’s (1996) claim that experienced translators allocate more cognitive resources to production strategies than novices. A key recommendation of this study is the need for specialized training for novice interpreters, particularly in strategy development, to handle various CI process challenges. Training focused on enhancing note-taking skills is vital, especially for trainees, as clear and understandable notes are crucial for success in the note-decoding and reformulating stages of CI.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| 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".