Bibliographic record
Abstract
This paper attempts to explore the different strategies of questioning in courtroom discourse, by highlighting the various discursive structures employed to form a question between courtroom interlocutors. More specifically, this research looks at the techniques employed in courtroom cross-examination to persuade the judge(s) to accept attorneys' accounts of what happened as well as the effectiveness of responses in fending off the influence and power of barristers. The corpus of this study is taken from 3 testimonies of prosecution witnesses in the trial of Timothy McVeigh concerning the Oklahoma City Bombing in 1997. By employing both quantitative and qualitative methods, the study investigates six questioning patterns, including wh-questions, yes-no questions, tag questions, so-questions, say-questions, and declarative questions. The study reveals that some types of questions used in courtrooms are strategically utilized to persuade juries and judges, confirm a piece of information, clarify an argument, threatening witnesses’ face, manipulate and/or coerce interlocutors within courtrooms. The paper also reveals that questioning is not only used to instigate an answer or a response but also to communicate information and draw conclusions.
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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 | 0.018 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".