Bibliographic record
Abstract
Using questions strategically to control witness testimony is imperative to a successful criminal trial. Witnesses are not without power and can deploy resistance strategies in the face of controlling questioning. Through an examination of question typology, question function, and answer types, this paper aims to provide a holistic understanding of how counsel and witness negotiate narrative production through the unique turn-taking system present in witness testimony. Rachel Jeantel’s testimony, in the case of Florida v. Zimmerman, was analyzed to explore the relationship between question types, question functions, and type-conforming or resisting answers. Results are in line with general counsel strategies for direct and cross-examination; counsel prefer more controlling questions, with a higher relative proportion of controlling questions in cross relative to direct examination. Type-conforming responses are the most common response in both types of examination. Resistance strategies employed by the witness are more common in cross-examination. However, there exist interesting dynamics between avoidance, correction, and confirmation-eliciting questions. Finally, the presence of question clusters and interruptions may contribute to narrative control and resistance to such control.
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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 | 0.015 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".