Bridging Communication Gaps in Crisis: A Case Study of Remote Interpreting in the Middle East During the COVID-19 Pandemic
Bibliographic record
Abstract
This study investigates the perceptions of remote interpreters regarding the impact of the transfer of interpreting mode from on-site mode to online mode. The study utilized an online survey and disseminated it online via online platforms, targeting interpreters in Middle Eastern countries. The survey collected information about the primary mode of remote interpreting practice, the frequency of interpreting services during COVID-19, the leading interpreting platforms, and major remote interpreting clients. It also gathered information about the impact of the COVID-19 pandemic on interpreting services, the challenges of the COVID-19 pandemic on interpreting services, and recommendations for the future of remote interpreting during global crises and emergencies. The study found that most interpreting services are via Zoom, Telephone, and Kudo. Moreover, the major clients for remote interpreting were healthcare providers and international organizations. On the other hand, the study revealed that the main impacts of COVID-19 on interpreting were the transition to remote interpreting services, cancellations and postpones of interpreting events, economic impact (a decline in income), security, data privacy, and confidentiality. Moreover, the main challenges were technological limitations, lack of non-verbal communication, and physical and mental health. The study recommends that it is imperative to develop resilient systems that efficiently integrate remote interpreting into crisis response strategies.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".