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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".