An SFL-based model for investigating explicitation-related phenomena in translation
Bibliographic record
Abstract
This paper proposes a model for investigating explicitation, implicitation, and explicitness in translated texts. The paper highlights the need to distinguish clearly between explicitation and other kinds of translation shifts. Specifically, when comparing target text (TT) renderings with the corresponding source text (ST), the model does not assume correspondence between shifts (and non-shifts) in ideational content and explicitation status. Nor does it assume correspondence between the overall explicitation status of renderings and the explicitness of the TT as a whole from the perspective of the relevant register in the target language (TL). The model draws on systemic functional linguistics to develop procedures for a three-phase analysis of these different explicitation-related phenomena. The parameters of traceability, realisational congruency and delicacy are applied to determine explicitation status, seen as arising from choices made by the translator within the systemic potential of the TL. Explicitness is determined from the perspective of registerial instantiation by comparing frequencies of different types of rendering with those found in a comparable corpus of TL non-translations. A case study, in which the model is applied to an English-to-Arabic translation of manner of motion verbs in a literary genre, demonstrates how each phase yields new insights, from a different perspective, while providing input for comparative analysis of the choices available in the two language systems with regard to the linguistic feature of interest.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".