Investigative interviews with online sexual offenders: A discursive analysis of verbal cues of deception
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The objective of this study is to investigate how individuals convicted of online sexual crimes use language to deceive police investigators during investigative interviews. Discursive and interactional cues that may indicate deception were identified based on the suspects’ responses to questions asked by the police investigator, that is, whether the questions had a high or low potential to elicit deceit. To refine the selection of deceptive answers to be analyzed, the information that was contradicted or questioned during the interview was then targeted for each suspect. The results show that seven elements – grammatical negations, argumentative markers, uncertainty markers, use of the conditional, ignorance markers, sincerity markers, and volubility – distinguish between truthful and deceptive responses. Findings suggest that these elements are used as strategies to convince the investigator of the truthfulness of the (deceptive) statements being made and to avoid providing information that could be refuted during the interview.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it