1200 paths and counting: A script analysis of firearms trafficking in the Province of Quebec, Canada
Bibliographic record
Abstract
Based on the crime script approach, the main objective of this study was to identify steps involved in firearms trafficking in the Province of Quebec, Canada. Our analysis focused on actions performed by actors, facilitating conditions, obstacles and errors for each step of the firearms trafficking process. A deductive thematic analysis was conducted to build the firearms trafficking script with 76 investigation files, conducted between 1996 and 2020, that were provided by the Quebec State Police. Firearms trafficking included six steps: (1) preparation; (2) acquisition of firearms; (3) storage of firearms; (4) search for customers; (5) transaction; and (6) exit. Since each step can be completed with different actions, a total 1200 combinations of actions could be used to traffic firearms. Results also indicated that several actors were involved at different steps of the script such as suppliers, middlemen, and vendors. Unregulated tools (e.g., hydraulic press, mold), materials and components (e.g., steel sheets, barrels) facilitated the fabrication of private firearms, while advertising firearms on social media was an error made by some suspects. The dynamic and sequential nature of firearms trafficking was highlighted by our script analysis. Crime script analysis also proved to be a useful approach to predict potential crime displacement, plan program evaluation and implementation, and prioritize prevention measures involving multiple agencies. • Firearms trafficking includes six steps from the preparation up to the transaction. • Each step can be completed with multiple actions. • Unregulated tools and pieces facilitate the fabrication of private firearms • 21 % of all trafficking cases included 20 firearms or more.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".