Detection of Deceptive Speech Acts in Chinese Courtroom Trials
Bibliographic record
Abstract
Confronted with a trial, litigants tend to focus on disputed facts, and when their interests are threatened they may resort to deceptive statements in order to obtain a judgment in their favor. Making deceptive statements in the court, referred to in this paper as deceptive speech acts (henceforth, DSA), will affect court’s judgment, waste judicial resources and harm social justice. Therefore, detection of deceptive speech acts (henceforth, DDSA) is of considerable forensic interests in improving efficiency of courtroom trials and enforcing the authority of legal system. Based on seven authentic cases of Chinese courtroom trials, this study attempts to find out reliable linguistic deception indicators in Chinese courtroom context and establish a model of DDSA. As one of efficient cues to deception, linguistic manipulation enables liars to take deception strategies (i.e., concealment, falsification and distortion). Drawing on the notion of linguistic manipulation, a coding scheme is established, which shows that deception strategies are principally realized by six linguistic indicators (vagueness, generality, intensifiers, formulaic expressions, references to the other, and minimizing markers). Linguistic analyses are made to present how DDSA is achieved in each extract. This research sheds light on data-based studies on DDSA, and offers implications for other judicial practices, like police interrogations, and prosecutor’s questioning.
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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 | 0.002 | 0.093 |
| 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 | 0.000 |
| 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".