The role of sexual interests and situational factors on rapists' <i> <b>modus operandi</b> </i> : <b>Implications for offender profiling</b>
Bibliographic record
Abstract
Purpose. Although it has often been suggested that there is a direct relationship between an individual's sexual interests and behaviours exhibited during the crime they commit, few studies have investigated this question empirically. The purpose of the present study was thus to examine the role of sexual interests and situational factors as to their possible relationship to three components of rapists' modus operandi : (a) the level of organization of the offence, (b) the level of force used by the offender, and (c) the level of injury inflicted on the victim during the sexual assault. Methods. This study is based on a sample of 118 offenders who sexually assaulted a female aged 16 or over. All participants were assessed phallometrically and through the CQSA, a computerized questionnaire. Data were analysed using multiple regression analyses. Results. Our findings showed links between sexual interests, situational factors, and rapists' modus operandi . Firstly, individuals demonstrating a greater sexual interest in nonsexual violence showed a higher level of organization in the modus operandi . Secondly, alcohol consumption prior to the offence was related to a higher level of coercion. Finally, a negative emotional state prior to the crime was related to a high level of injury inflicted on the victim. Conclusions. Despite the fact that several authors postulated a direct link between the offender's sexual interests and his behaviour at the crime scene, our results only partially support this hypothesis. Moreover, our results partly support the fact that crime scene behaviour is related to offenders' personal characteristics, challenging an assumption of criminal profiling. We still believe that the modus operandi is related to offenders' personal attributes. It is, however, dynamic and may fluctuate due to certain situational factors related to offenders and victims. Future studies should take into account situational factors related to offenders and their victims.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".