A Psycholinguistic Approach to Consecutive Interpretation: Identifying Problems Among Saudi Interpreters
Bibliographic record
Abstract
The complexities associated with the interpretation process have raised considerable attention from researchers. The multifaceted nature of interpretation, which involves transferring meaning from one language to another in real time, presents a range of cognitive, linguistic, and practical challenges. This study comprehensively examines problems encountered by Saudi interpreters while performing consecutive interpretation. The analysis is grounded in Gile’s Effort Models (1995), which investigates the cognitive mechanisms underlying challenges across four phases: listening and understanding, note-taking, note-decoding, and expressing and reformulating. Using a questionnaire as the primary data collection tool, the study applies quantitative analysis to investigate the reported problems among 102 trainee and professional Saudi interpreters. The study reveals insights into the problems encountered by the participants during different phases of consecutive interpretation, such as note-taking, coherence maintenance, handling information density, managing nervousness, and ensuring memory reliability. These findings align with previous empirical studies in the field, emphasizing the importance of understanding the cognitive and practical difficulties inherent in the process of consecutive interpretation. Overall, this study contributes to the existing knowledge base on interpreter challenges while also highlighting the universal nature of these difficulties and the need for customized ongoing interpreter training programs.
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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 | 0.010 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".