Bibliographic record
Abstract
English is de facto reinforcing its role as the language of international legal communication. Indeed, while different national languages continue to play a crucial role in the definition, the execution, and the application of the law, English is increasingly employed by non-native legal professionals worldwide. Thus, this study focuses on the use of English as a Lingua Franca (ELF) in legal settings and aims to offer considerations towards the conceptualization of Legal English as a Lingua Franca (LELF). As English is considered a global asset in legal communication, it is argued that a finer problematization of LELF is imperative. In this respect, the study also discusses whether it is possible to apply the concept of a lingua franca to legal language tout court or whether the distinctive features of legal discourse across systems make the definition of LELF inapplicable from a conceptual perspective. This article also offers a reflection on the main concerns which arise regarding the widespread use of English in legal settings, especially in the light of the specificities of different legal systems, legal cultures and communities of practice. Thus, all stakeholders involved should adopt a more reflexive approach in order to go beyond the unproblematic acceptance of LELF across legal settings and to be more aware of the implications and consequences that its usage entails.
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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".