Translating the Bahá'í Writings into Languages Other Than English
Bibliographic record
Abstract
Given its belief in the transformative power of the Word of God, the Bahá’í Faith places great importance on the translation of its sacred writings into as many languages as possible. Translations into languages other than English need to be approved by the National Spiritual Assembly of the country in which they are published, but are often initiated by individuals, meaning that institutions and individuals have distinct and complementary roles in the translation process. Most of these translations are from English versions—usually those produced by Shoghi Effendi—of the original Bahá’í writings in Arabic, Persian, and Turkish. As linguists who have been involved in translating and reviewing translations of the writings, the authors have encountered a number of challenges in their translation work, including questions about spelling, terminology, and the politeness strategies employed in the original work, as well as idiosyncrasies of English usage. We illustrate these issues and possible approaches to dealing with them using the case of a short passage from Bahá’u’lláh’s Writings translated into Japanese and Tok Pisin. It is hoped that this article will lead to exchanges among translators and reviewers and possibly to the development of a database of exegesis literature on the Writings and other aides to translators.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".