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Record W3215007618 · doi:10.21125/iceri.2021.0951

DISCOURSE ANALYSIS OF NATIONAL PECULIARITIES OF JUDICIAL COMMUNICATION IN THE SUPREME COURT OF CANADA

2021· article· en· W3215007618 on OpenAlexaboutno aff
Elena Dmitrieva

Bibliographic record

VenueICERI proceedings · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicLaw in Society and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsLingua francaPolitical scienceLinguisticsPoliticsReputationSupreme courtSociologyNational languageLaw

Abstract

fetched live from OpenAlex

The English language is recognized as a polycentric language, which means that it is spoken in a number of countries. National variants of English have their own linguistic peculiarities manifested at the phonetic, lexical and grammatical levels. On the other hand, the English language has gained the reputation of a modern Lingua Franca, used as a means of communication in the fields of international business, trade, law, politics and culture. People learn English in order to be able to establish relations with foreign partners as this language opens the doors to transnational cooperation. However, there appear to be some questions concerning the choice of the national variant of English that must be taught to students. Traditionally the British and American variants were preferred to other national variants. The survey, carried out in major Russian universities, showed the tendency to teach above mentioned languages as models of English. It is quite understandable as the influence of the UK and especially the USA on the international community cannot be disputed. Nevertheless, it is worth mentioning that from pedagogical perspective attention should be paid to other national variants of English, for example, Canadian English. This approach may be justified by the fact that Canada is an active participant on the international arena and a fully-fledged member of various intergovernmental organizations. It is fair to say that if you acquire a good command of, for instance, American English you will be able to communicate successfully in such countries as Britain, Canada, Australia and New Zealand. However, you may come across some problems when it comes to specific spheres such as legal communication, for example, judicial discourse. This obstacle may be explained by the fact that the communication in a courtroom is characterized by strict rules and rituals which have been established for centuries. They embedded national peculiarities conditioned by historical development and the culture. These specific features are reflected in the way lawyers and judges communicate, because the language is the basis of law and legal profession. It makes judicial discourse to a great degree a unique socio-linguistic phenomenon which is closely related to the given state. Hence, it can be presumed that the teaching of the English for specific purposes, legal English in particular, should be nationally oriented. The aim of this article is to examine the national peculiarities of courtroom communication in Canada by means of discourse analysis. The features which will be studied are the following:1) judicial concepts and terminology of Canadian Supreme Court discourse;2) grammatical peculiarities inherent in judicial communication in Canada;3) communicative strategies followed by Canadian Supreme Court judges and lawyers. The results of this research may be implemented in the process of teaching English language for specific purposes, for discourse analysis helps to reveal and interpret national linguistic characteristics of professionally-based communication.

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.104
Threshold uncertainty score0.754

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.027
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0060.007
Science and technology studies0.0230.011
Scholarly communication0.0120.004
Open science0.0020.004
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.021
GPT teacher head0.305
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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