Understanding wildlife crime in China: Socio-demographic profiling and motivation of offenders
Bibliographic record
Abstract
Wildlife crime presents a growing threat to the integrity of ecological communities. While campaigns have raised consumer awareness, little is known about the socio-demographic profile of wildlife offenders, or how to intervene. Using data from China Judgements Online (2014-2018), we documented 4,735 cases, involving 7,244 offenders who smuggled, hunted, transported, sold and/or purchased protected species in contravention of China's Criminal Law. Offenders were predominantly men (93.0% of 7,143 offenders), aged 30-44 (43.9% of 4,699), agricultural workers (48.4% of 3,960), with less schooling (78.6% of 4,699 < senior secondary school). Socio-economic profiles related to crime seriousness, the type of illegal activity, motivation and taxon involved. These generalizations reveal scope to tailor specific intervention and mitigation approaches to offender profiles, through public information campaigns, proactive incentives opposed by punitive disincentives, and provision of alternative incomes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".