Bibliographic record
Abstract
Despite the fact that legal discourse is intended to be clear, precise and unambiguous, in legal terminology there are obvious signs of cultural variability that can be observed not only in different languages, but also in varieties of the same language. Ignorance of cultural differences in legal terminology and legal discourse can lead to serious complications in an intercultural context. This study is limited to terms of reference and forms of address to judges of different levels in the British, Irish, American, Canadian, Australian and New Zealand varieties of English in a courtroom setting. The goal of the study is to analyze the terms of references and forms of address to judges in these varieties of English, identify their similarities and culture specific features and try to find the reasons for the differences. The data were obtained from various sources: dictionaries, legal documents, newspapers, as well as some secondary sources (Brown & Rice 2007, Hickey 2008, McPeake 2010) and Internet resources. They were analysed drawing on studies of pluricentric languages (Clyne 1992, Kloss 1967, Leitner 1992, Muhr & Marley 2015), World Englishes Paradigm (Bolton 2006, 2017; Crystal 2003, Domashnev 2000, Kachru 1985, 1986, 1988, 2008; Low & Pakir 2017, Proshina 2012, 2017, 2019); implementing comparative, semantic, pragmatic, discursive and cultural analysis. To explain some of the results, the legal and political systems of the countries that speak the national varieties of English were analysed. Preliminary results of the study revealed both similarities and differences in the terms of reference and forms of address to judges of various ranks, caused by a nexus of historical, political and social reasons that require further study. Among these, one can note the degree of openness of society to the democratization of its legal system, the country's desire to either follow the traditions established in British judicial discourse, or to demonstrate their uniqueness and independence from the former colonial power. Despite its limited nature, the study provides some new data showing that the lexical and discursive variability observed in the legal sphere contributes to the formation of varieties of pluricentric languages. The results can contribute to the study of pluricentric languages, find application in lexicographic practice, as well as in the teaching of legal English to law students.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".