Bibliographic record
Abstract
This paper tries to explore the different argumentation strategies in courtroom discourse. The paper aims to decode the various argumentative strategies that are employed to communicate a successful interaction between the interlocutors in courtrooms. This is done by highlighting the linguistic tools targeting the persuasion of the conversationalists in the legal discourse presented in courts. The paper will focus on five strategies of argumentation, including lexical choices, questioning and answering, oppositional arguments, rhetorical questions, and premeditated arguments. The core concern of the investigation of these argumentation strategies is to show the degree to which they are used by courtroom interlocutors to communicate a successful and persuasive argument to their recipients. Data used in this study are derived from two legal trials: Nelson Mandela’s trial and Bill Clinton’s trial. The research questions of this paper are: first, what is meant by argumentation in courtroom discourse? Second, what are the different argumentation strategies used in courtroom discourse? Third, to what extent are argumentation strategies employed to achieve a persuasive argument between interlocutors in courtrooms? There are three main findings in this paper: First, attorneys and litigants use various argumentation strategies to influence their recipients so as to be able to persuade the court of their arguments. Second, the power of persuasiveness is entirely based on the ability to use various strategies of argumentation. Third, language is a crucial element in the understanding of legal arguments in courtrooms.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".