A response to: Morselli, C., Turcotte, M. and Tenti, V. (2010)<i>The Mobility of Criminal Groups</i>
Bibliographic record
Abstract
The degree to which organised crime groups extend their activities and influence into new geographic areas is a major concern for law enforcement officials and policymakers worldwide. Over the past decade, a number of researchers have conducted specialised studies and reviews of this phenomenon, and have offered a number of explanations of its underlying drivers. Recently, Morselli, Turcotte, and Tenti were commissioned by Public Safety Canada to prepare a report on this topic, The Mobility of Criminal Groups, which reviewed several case studies and prior commentaries and, based on an inductive (evidence-based) process, offered a conceptual framework for understanding how organised crime groups come to establish themselves (successfully or unsuccessfully) in places outside their area of origin. The current discussion article consists of a written response to Morselli et al.’s report, reflecting on their position in light of recent research on Vietnamese organised crime in the United Kingdom.
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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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.036 | 0.042 |
| Insufficient payload (model declined to judge) | 0.023 | 0.014 |
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".