A Study on Vagueness Used by the Defendant in Courtroom Discourse from the Perspective of Adaptation Theory
Bibliographic record
Abstract
Court discourse is a typical legal language, recently arousing scholars’ interest. In the courtroom discourse, many language strategies would be applied in this special setting out of various purposes and vagueness is one of them. With the transcripts from the Jodi Arias’ murder case as the database, this paper investigates vagueness in the American courtroom from the perspective of adaptation theory, paying specific attention to the defendant. Two conclusions are reached: (1) the defendant uses vagueness commonly to adapt to the mental world, the social world and the physical world. As to the mental world, it can be divided into speaker-directed adaptation and hearer-directed adaptation. As to the social world, the study analyzes how the defendant adapts to the legal obligation. Defendant also adapts to the physical world in courtroom settings. (2) the study finds four pragmatic functions of vagueness used by the defendant in the courtroom discourse, they are 1) increasing the credibility of utterance; 2) avoiding absolute utterance; 3) providing appropriate information; 4) Being polite.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".