CONCEPTUAL METAPHORS AS AN INTEGRAL PART OF JUDICIAL DISCOURSE IN CANADA AND THE USA
Bibliographic record
Abstract
Metaphors are believed to make our speech and writing more colourful and expressive and are commonly associated with poetry and fiction. However, in everyday communication these tropes are used as well and have become its integral part. For instance, in a short mundane dialogue two speakers may use idioms to convey the ideas and notions more clearly. Thus, we can maintain that metaphors are not only the tools of poets, novelists, and writers, but a linguistic means which is common to all styles and genres. Moreover, metaphors are considered to be the language elements which reflect the national identity and mentality and in a condensed way preserve the national values and beliefs. They form the system of notions which are unique and specific for each country. Metaphors may also be recognized as cultural heritage of the nation, the most vital part of national world-image. It is known that teaching a foreign language besides pure linguistic aspects implies the transference of cultural peculiarities of the country or region where this language is spoken. The students who want to succeed in intercultural communication should acquire skills in pronunciation, vocabulary, and grammar and, in addition, study conceptual metaphors which may assist them in understanding the national character and mentality of the country which language they are studying. Such an approach will form the basis of students’ linguistic competence and make them qualified professionals. The aim of this article is to share the research results which were obtained during the discourse analysis of courtroom communication in Canada and the USA. The analysis consists of quantitative examination, the purpose of which is to reveal the frequency of metaphors and idioms in judicial discourse, and qualitative examination, which implies the study of national concepts and values underlying metaphorical expressions. The article contains a table where the metaphors are categorized according to the main concepts which they convey as well as their literal meaning. Furthermore, it is worth mentioning that the research results are of high pedagogical importance as they may be included in teaching manuals and textbooks. For instance, this material may be used as reference by both students and professors, who teach legal English as a second language.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.022 | 0.018 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 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".