Confessions of sex offenders: extracting offender and victim profiles for investigative interviewing
Bibliographic record
Abstract
Purpose Although offender profiling has been cited as an effective tool to interview suspects, empirical profiling methods have completely excluded interviewing suggestions when testing the validity of this technique. The purpose of this paper is to explore the utility of empirically derived profiles of offender- and victim-related sexual assault case characteristics (n=624) in the preparation of the interrogation strategies in sexual assault investigations. Design/methodology/approach Latent class analysis was used to extract profiles of offender- and victim-related sexual assault case characteristics in a sample of 624 incarcerated sex offenders. Moreover, relationships between offender and victim profiles were conducted using χ2analyses. Findings Findings show that specific offender-victim profiles are related to greater likelihood of confession during the interrogation. Possible interrogation strategies for each profile are suggested and implications for the practice of interviewing suspects are discussed. Originality/value The study is the first to examine both victim and offender profiles in order to assess the significant victim-offender profile combinations and their associated probabilities of resulting in confession.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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 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".