An action phase approach to offender profiling
Bibliographic record
Abstract
Purpose Continued debate surrounds whether or not offender profiling is a valid practice. Critics have mainly contended that few studies have produced clear, quantifiable, evidence of a link between crime scene actions (A) and offender characteristics (C). Arguing that this is due to a failure to study offender actions as part of a dynamic decision‐making process, this study sought to identify action phases that are representative of the general decisions an offender must make during the commission of a sexual offence, and relate these decisions to known characteristics of the offender (C). Methods Two‐step cluster analyses were performed on data from 347 stranger sexual offences, committed by 69 serial sexual offenders, by action phase: (1) search ; (2) selection ; (3) approach ; (4) assault ; and also for an offender's (5) characteristics . Multiple correspondence analysis ( MCA ) was then utilized to investigate the inter‐relationship of action phase clusters and offender characteristics. Results The MCA results indicated that specific behavioural macro‐clusters formed across the various actions phase and offender characteristic clusters in a meaningful way. Additionally, the macro‐clusters themselves corresponded to the extant literature on sexual assault, revealing several points of congruence between offender crime scene actions and offender characteristics. Conclusion The results of the study demonstrate that when crime scene behaviours are interpreted within a dynamic decision‐making process (i.e., utilizing action phases), reliable and valid empirical links may potentially be drawn between an offenders behavioural actions and their characteristics.
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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 | 0.011 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".