Assessing the success factors of organized crime groups
Bibliographic record
Abstract
Purpose – Expert assessment of organized crime (OC) group capabilities is often the basis for national threat assessments; it is rare, however, for variations in collective expert opinions of OC success factors to be systematically evaluated. The purpose of this paper is to examine the differences in how 150 criminal intelligence experts from a variety of national and organizational backgrounds sort and organize perceived attributes for OC group success. Design/methodology/approach – The paper uses the Royal Canadian Mounted Police (RCMP) Sleipnir framework as a foundation for a Q-sort survey regarding the characteristics of OC group success. The survey was delivered to over 150 criminal intelligence specialists at a national conference in 2011. Descriptive statistics, seemingly unrelated regression, and biplots reveal different aspects of survey responses. Findings – Results show that perceptions of the ingredients for OC group success both vary by nationality and by analysts’ level within the hierarchy of the law enforcement structure (local, state, national). These differences are marked; particular characteristics are viewed as differentially important for the perceived success of OC groups. Furthermore, the results suggest that there are shared and structured differences in perceptions of OC group success characteristics. Research limitations/implications – The survey has identified distinct differences between the characteristics for OC group's success perceived by analysts in the USA, Canada, and beyond. Furthermore, the organizational level of the analyst (local, state, national) shapes the perceptions of success factors. It is possible variations identified merely reflect differentials in training and experience, i.e. different organizational perceptions of the same problem. That aside, the patterning of results seem likely to be based to some degree on external factors linked to OC group operations, and not just on individual characteristics of the surveyed intelligence professionals. Practical implications – The current research raises a number of questions regarding the confidence that should be placed in OC group assessments. The research has highlighted areas of professional dissonance that were not apparent from the RCMP Sleipnir research alone. Causes of the dissonance in assessments, and connections of these variations to both intelligence analysts’ experience, training, and organizational ethos; and to OC group capabilities, seem deserving of additional attention. Originality/value – Expert intelligence analyst interpretation of OC group capability is central to most national risk and threat assessments, yet the assessment processes themselves are rarely examined. This is a unique survey of over 150 intelligence personnel that highlights significant differences in perceptions of OC groups, differences that raise questions about how the authors evaluate the OC threat.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".