Validities and Abilities in Criminal Profiling: A Critique of the Studies Conducted by Richard Kocsis and His Colleagues
Bibliographic record
Abstract
In a recent issue of this journal, Kocsis reviewed the criminal profiling research that he and his colleagues have conducted during the past 4 years. Their research examines the correlates of profile accuracy with respect to the skills of the individual constructing the profile, and it has led Kocsis to draw conclusions that are important to the profiling field. In this article, the authors review the contributions of the Kocsis studies and critique their methodological and conceptual foundations. The authors raise a number of concerns and argue that data from the Kocsis studies fail to support many of the conclusions presented in his recent review. The authors present evidence in support of their assertions and provide recommendations that will allow future research in the area to generate data that are more meaningful and generalizable.
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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 | 0.166 | 0.309 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.018 | 0.014 |
| Science and technology studies | 0.010 | 0.105 |
| Scholarly communication | 0.018 | 0.025 |
| Open science | 0.009 | 0.010 |
| Research integrity | 0.016 | 0.033 |
| Insufficient payload (model declined to judge) | 0.001 | 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".