English-Arabic-English Translation: A Novel Methodological Framework for the Standardisation of Translation Parameters
Bibliographic record
Abstract
It is evident to see that, in the field of translation, there is a random use of the terms ‘method’, ‘approach’, ‘strategy’, ‘procedure’ and ‘technique’ by both teachers and students alike. This article attempts to shed light on such phenomenon and to bring more clarity and objectivity to the world of translation by suggesting a standardised methodological framework. English-Arabic-English translation examples and a questionnaire filled by university Arabic-speaking students and teachers were used for analysis and discussion. Results of the analysis and discussion of samples and the questionnaire in this paper have indicated that there is an urgent need for a novel methodological framework in order to form a standardised profile for the use of translation parameters such as ‘method’, ‘approach’, ‘strategy’, ‘procedure’ and ‘technique’. To achieve this objective, a proposed methodological framework was made for use by students, teachers and those interested in carrying out further research in this field.
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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 | 0.301 | 0.348 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".