An Empirical Examination of the Victim-Search Methods Utilized by Serial Stranger Sexual Offenders: A Classification Approach
Bibliographic record
Abstract
Past research on the spatial mobility of serial offenders has generally found that these individuals make calculated decisions about the ways in which they come into contact with suitable victims. Within the geographic profiling literature, four victim-search methods have been theorized that describe how serial predatory offenders hunt for their victims: hunter, poacher, troller, and trapper. Using latent class analysis, the aim of this study is to test whether this theoretical typology can be empirically derived using data that were collected from both police files and semi-structured interviews with 72 serial sex offenders who committed 361 stranger sexual assaults. Empirical support is found for each of the aforementioned victim-search methods, in addition to two others: indiscriminate opportunist and walking prowler. Chi-square analyses are also conducted to test for associations between this typology and characteristics of the offense such as victim information, environmental factors, and the offender’s modus operandi strategies. Findings from these analyses suggest that the types of victims and environments targeted by the offender, as well as the behaviors that take place both before and during the offense, are dependent upon the offender’s victim-search strategy. Although the theoretical hunter, poacher, troller, and trapper were intended to describe the victim-search methods of serial violent predators more generally, the finding that these strategies exist along with two others in this sample of sexual offenders may indicate that search behavior is specific to certain crime types. Furthermore, these findings may be of assistance in the investigation of stranger sexual assaults by providing law-enforcement officials with possible clues as to the characteristics of the unknown suspect, the times and places likely targeted in any past or future events, and possibly even his base of operations.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".