Translation Quality Assessment of Diaphasic Intralingual Translation: A Functionalist Approach
Bibliographic record
Abstract
This article proposes a Translation Quality Assessment (TQA) framework for a particular type of intralingual translation, namely the interregisterial transformation of specialized texts into ones suitable for a lay target readership, also known as diaphasic intralingual translation. Based on a functionalist linguistic and translation-theoretical approach, this framework is designed to assess and promote registerial adequacy in diaphasic intralingual target texts. The article associates this adequacy with maximum transparency of meaning in target texts and identifies five different principles as more specific quality criteria: (1) iconicity between meanings and wordings, (2) familiarity of lexis, (3) explicitness of meanings, (4) groundedness or concreteness, and (5) coherence. These five principles are in some cases subcategorized and in all cases defined in terms of linguistic manifestations. Examples are drawn from the lay-oriented genre known as Patient Information Leaflets, i.e., the small brochures included with medicinal products that provide information and instructions to the user. Most cases include both “positive” and “negative” examples, i.e., instances of both adherence and non-adherence to a given principle. Many of the “negative” examples are followed by suggestions for improvement.
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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 | 0.050 | 0.094 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".