Translation: Bridging the Gap, or Creating a Gap to Bridge? Reflections on the Role of Translation in Bridging and/or Widening the Gap between Different Cultures with Particular Reference to Religion and Politics
Bibliographic record
Abstract
This paper attempts to explore the problems involved in the Translation of culturally, politically and/or religiously Charged Words and Expressions with particular reference to religion and politics. Most translators and translation scholars are probably familiar with the concept of translation as a major tool used to bridge the gap between different cultures. Nevertheless, is that always the case? Does translation always help bridge the gap, or does it, sometimes at least, widen it, if not even create a new one? With the emergence of modern linguistics in the twentieth century and translation studies a few decades later, a translator was no longer regarded as a creative writer in his/her own right; he/she is basically an honest conveyer of somebody else's message. The more 'invisible' a translator is the better credit he receives. Nevertheless, is this always the case? This researcher discusses the topic with reference to a set of words and expressions of particular interest to the present conditions in the Middle East area.
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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 | 0.040 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.018 | 0.063 |
| Scholarly communication | 0.018 | 0.040 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 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".