Disentangling and demystifying converging crimes and illegal wildlife trade in South Africa, Hong Kong, and Canada
Bibliographic record
Abstract
Illegal wildlife trade is increasingly reported as intersecting with other serious crimes, a phenomenon labelled “crime convergence”. However, there is limited empirical research on the extent and nature of these linkages. The present study seeks to understand what criminal activities converge with illegal wildlife trade, how they converge, and what factors shape crime convergence. One hundred and twelve law enforcement personnel and other experts were interviewed, predominantly in three focus jurisdictions: South Africa, Hong Kong, and Canada. Our results showed that there is evidence of illegal wildlife trade converging with a multitude of illegal activities, including drug trafficking; sex trafficking; child abuse; trafficking in human body parts; migrant smuggling; forced and bonded labour; illegal alcohol trade; arms trafficking; vehicle theft and trafficking; illegal trade in counterfeit and pirated goods; and illegal trade in mined resources, among others. Interviewees who have led large investigations confirmed that convergence is the norm, but its nature depends on the species, location, organised crime group, and stage of the supply chain. Convergences can range from opportunistic and ad hoc, to sophisticated and sustained. For the latter, adopting an organised crime approach is essential to counter illegal wildlife trade and dismantle the criminal networks involved. • Wildlife trafficking converges with many serious crimes in South Africa, Hong Kong, and Canada • Converging crimes include trafficking in drugs, arms, persons, body parts, mined goods, etc. • Convergence may involve bartering, product convergence, forced criminality, among many others • Convergence does not necessarily mean that both or either crime involved is highly organised
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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 | 0.000 | 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 | 0.000 | 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".