Tracing Fidelity to the Discursive Field and Aesthetic Adequacy in Translation: A Transcultural Perspective
Bibliographic record
Abstract
There are established internationally recognised standards of assessing translation quality; however, it is the means of determining their appropriateness and acceptability in different social contexts that is debatable. The article traces discourse fidelity through some selected linguistic and aesthetic criteria of compliance with the standards of “accuracy”, “adequacy”, “correctness”, “correspondence” and “fidelity” in the target language translation process. These criteria are then tested for aesthetic equivalence through the analysis of the translation of the historically compelling text, the Luganda evangelical epic TukutenderezaYesu (We praise you Jesus) of the international Anglican Revival Movement into a modern Runyankore video-recorded and choreographed version. To this end, the author draws on cultural semiotics, functionalist and textual theoretical models that take translation quality assessment beyond linguistic acceptability. Among other findings, one note that beyond the translator’s linguistic skills, the emphasis in tracing discourse fidelity and aesthetic adequacy in translation, needs to be placed on the sensitivity to the discourse in question, the “situationality” of the translated text, the translator’s interpretative ability and the information/communication technology used to circulate the final product.
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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 | 0.062 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.009 | 0.078 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".