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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".