Challenges of Translating American and British Forensic Terms and Texts into Arabic
Bibliographic record
Abstract
This study explors the challenges of translating American and British forensic terms into Arabic when dealing with forensic legal terms. The study aims to explore of the challenges, proposed solutions, and types of dictionaries used in the translation process. The study followed a quantitative method approach by developing questionnaires. The sample of participants included 30 translators specializing in the legal context as forensic translators. They participated in a methodological survey that covered various aspects of their professional experience. The results revealed significant differences in the translation of criminal legal terms among translators, highlighting the importance of understanding the meaning when written in British or American English. The study also sheds light on the translators’ suggestions for addressing the challenges identified. The results highlight the importance of improving the translation process in the specialized field of forensic terms in the legal context. Additionally, the study recommends the importance of using specialized dictionaries after providing continuous training and qualification to enhance translators' abilities in translating the complexities of criminal evidence within the legal framework. Finally, the study provides valuable insights to enhance the translation of criminal legal terms and improve overall efficiency in this required field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".