Metaphorization of the Trial Using War Terminology in the American and Canadian National Variants of Judicial Discourse: A Comparative Analysis
Bibliographic record
Abstract
Cognitive linguistic research into conceptual metaphors is the focus of attention of many scientists. These studies proceed from the postulate of cognitive linguistics which states that the world around us is categorized and structured according to certain cognitive models. These models are often based on the mapping of properties and elements of one concept onto the domain of another concept by virtue of similarity. Thus, conceptual metaphors are created, which are linguistic representations of the cognitive processes of human mind. According to G. Lakoff and M. Johnson’s definition, the metaphorical model “argument is war” is a linguistic manifestation of such cognitive associations. This article presents the results of the study that aimed to determine the extent to which the metaphorical model “trial is war” can be applied to the American and Canadian national variants of judicial discourse, as well as to identify key elements underlying the metaphorical mapping of the concept of war onto the concept of trial. In addition, a comparative quantitative analysis was performed to determine which national variant of judicial discourse contains more metaphors based on the “trial is war” model. The following sources served as the material for the study: judgements of the courts of the United States and Canada, transcripts of the countries’ highest judicial bodies, as well as articles by American and Canadian experts on law and legal proceedings. In order to conduct the quantitative and qualitative analysis, the material was arranged in two monolingual corpora, which were processed using the AntConc software.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".