Question types in online sex offender interviews: unveiling the influence on information retrieval
Bibliographic record
Abstract
Purpose The present study aims to observe interviewing practices using real-life suspects’ interviews of online child sex offenses and explore the impact of the formulation of questions on investigative relevant information (IRI). Design/methodology/approach A sample of 30 suspect interviews, French transcripts collected as part of a larger research project conducted in the province of Quebec, Canada, between 2007 and 2018, was analyzed. All the interviews were conducted by the Internet Child Exploitation (ICE) unit of the Quebec provincial police force. The study involves a two-step coding procedure: the identification of question types used by investigators during the interview and the responses and information (e.g. IRI) provided by the suspect following those different types of questions. Findings Based on the selected interview transcripts, the study results indicate that the investigator mostly used productive questions. The most effective questions to elicit a detailed account were open-ended and recall questions. The results, however, showed a limited use of open-ended questions by investigators. As for the IRI, action, item and person details were the most reported information by suspects in the sample. Originality/value While past research has focused primarily on describing the questions asked during investigative interviews, this research provides insights into the formulation of questions in real-life interviews of suspects of online child sex offenses and their effect on information gathering. By doing so, the results enhance the understanding of how the questions posed can influence suspects’ responses, providing valuable insights into how to elicit the most relevant information to effectively contribute to the investigation. The study’s conclusions are anticipated to have practical implications for investigative interviews and to support the advancement of sexual crime investigations.
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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 | 0.146 | 0.351 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".