The Importance of Knowledge Cumulation and the Search for Hidden Agendas: A Reply to Kocsis, Middledorp, and Karpin (2008)
Bibliographic record
Abstract
In a recent narrative review and meta-analysis of the criminal profiling (CP) literature, Snook, Eastwood, Gendreau, Goggin, and Cullen (2007 Snook, B., Eastwood, J., Gendreau, P., Goggin, C. and Cullen, R. M. 2007. Taking stock of criminal profiling: A narrative review and meta-analysis. Criminal Justice and Behavior, 34: 437–453. [Crossref], [Web of Science ®] , [Google Scholar]) found that self-labeled profilers and/or self-labeled profilers/experienced detectives did not decisively outperform lay individuals in their ability to produce accurate profiles. Combined with their finding that the CP literature is based largely on commonsense rationales, they cautioned police officers about using CP in investigations. In a recent issue of this journal, Kocsis, Middledorp, and Karpin (2008 Kocsis, R. N., Middledorp, J. and Karpin, A. 2008. Taking stock of accuracy in criminal profiling: The theoretical quandary for investigative psychology. Journal of Forensic Psychology Practice, 8: 244–261. [Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) (KMK) challenged some of the methodological decisions of Snook et al. and questioned their interpretation of their meta-analytic findings. In addition, KMK suggested that the work by Snook et al., and that of Bennell, Jones, Taylor, and Snook (2006 Bennell, C., Jones, N., Taylor, P. and Snook, B. 2006. Validities and abilities in criminal profiling: A critique of the studies conducted by Richard Kocsis and his colleagues. International Journal of Offender Therapy and Comparative Criminology, 50: 344–360. [Crossref], [PubMed], [Web of Science ®] , [Google Scholar]) is based not on a desire to move profiling research forward but on a hidden agenda to promote an investigative psychology (IP) approach to profiling. In response, we argue that KMK have failed to consider important issues of knowledge cumulation when criticizing the meta-analysis by Snook et al. and that KMK's views with respect to IP have no basis in our previous research. Instead of trying to find a nonexistent hidden agenda in our work, we suggest that efforts would be better directed toward conducting high-quality CP research.
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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 | 0.006 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".