Explicitation and Implicitation in Legal Translation – A Process Study of Trainee Translators
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article explores the use of explicitation and implicitation in the context of legal translation. Legal texts are linguistically complex and difficult to understand for lay persons. From a cognitive point of view it may therefore be assumed that ex- and implicitations will be frequent phenomena in legal TTs, because translators will tend to leave traces of their hard-won understanding in the TT. On the other hand, legal translations have legal consequences in the real world. From a legal point of view it may therefore conversely be assumed that ex- and implicitations will be relatively rare phenomena in legal TTs because of the potential legal effect of adding or removing information. But how is this schism reflected in legal TTs performed by translators at different levels of expertise? This article examines phenomena of ex- and implicitations in trainee translator TTs. It is hypothesized that lack of sufficient knowledge of legal scenarios will override heavy mental processing efforts and that trainee translators will restrict themselves to choosing only obligatory ex- and implicitations as their safe bet.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 it