Legal Language – Pragmatic Approaches to Its Interconnectivity with Legal Interpretation and Legal Translation1
Bibliographic record
Abstract
The purpose of this paper is to enhance existing insights into the complex relationship between legal language, legal interpretation and legal translation that challenges the participants of a particular domain-specific communicative situation. This situation can be described as a complex communicative-cognitive procedure taking into consideration a pragmatic approach to reach its addressees on a continuum from lay persons to experts. The analysis of some “typical” examples shows (not surprisingly) that different kinds of knowledge are necessary in order to achieve a felicitous communicative act in which a high degree of specialist knowledge is of paramount importance to a successful result both in intra- and interlingual translation. The particular legal language under scrutiny, in other words German contrastive to Norwegian, form the basis for the analysis of the activity of translation in which the translator must be able to understand/interpret the text and must be aware of not taking the words at their “face value” to be able to render their meaning in the target language.
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.
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 | 0.017 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.009 | 0.069 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".