Legal Translation Studies as Interdiscipline: Scope and Evolution
Bibliographic record
Abstract
This paper offers an overview of the development of Legal Translation Studies as a major interdiscipline within Translation Studies. It reviews key elements that shape its specificity and constitute the shared ground of its research community: object of study, place within academia, denomination, historical milestones and key approaches. This review elicits the different stages of evolution leading to the field’s current position and its particular interaction with Law. The focus is placed on commonalities as a means to identify distinctive reference points and avenues for further development. A comprehensive categorization of legal texts and the systematic scrutiny of contextual variables are highlighted as pivotal in defining the scope of the discipline and in proposing overarching conceptual and methodological models. Analyzing the applicability of these models and their impact on legal translation quality is considered a priority in order to reinforce interdisciplinary specificity in line with professional needs.
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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 | 0.095 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.025 | 0.028 |
| Science and technology studies | 0.006 | 0.037 |
| Scholarly communication | 0.027 | 0.025 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.005 | 0.006 |
| 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".