Classifying Serial Sexual Murder/Murderers
Bibliographic record
Abstract
Keppel and Walter’s (1999) classification system for serial sexual murder/murderers is sometimes used as the basis for generating offender profiles despite the fact that it has yet to be empirically validated. This model assumes that serial sexual murder/murderers can be classified into four categories—power-assertive, power-reassurance, anger-retaliation, and anger-excitation—according to the degree of anger and power exhibited by the offender in their criminal and noncriminal lives. Within the current study, assessing the validity of this model involved examining the crimes and backgrounds of 53 serial sexual murderers to determine if the categories proposed by Keppel and Walter could be identified. Proximity Scaling was used to examine the degree of co-occurrence between each and every behavior/characteristic. No evidence of highly co-occurring behaviors/characteristics from Keppel and Walter’s proposed categories was found, indicating that the classification system is potentially invalid. Results are discussed in terms of their theoretical and practical implications.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".