Evidence on the role of social media in the illegal trade of Iranian wildlife
Bibliographic record
Abstract
Abstract The combination of increasing trade across an ever more globalized world and the ubiquity of social media access has led to unprecedented levels of wildlife exploitation. In this study, we opportunistically surveyed Instagram and Telegram from 2019 to 2020, two of Iran's most prominent social media platforms, for advertisements of illegally captured wildlife in Iran. In total we documented 305 advertisements for 63 species, including birds (29%), amphibians (27%), reptiles (26%), and mammals (17%). Trade was most active in June, which may be due to increased availability of young animals, following spring births. The majority of the species advertised for sale (65%) were classified by the IUCN Red List of Threatened Species as Least Concern, and 5% of the species we documented as being traded are Endangered. Some Endangered species advertised for sale were Caspian seal and Saker falcon. While the sale of all species is illegal in Iran, 25% of species were also listed on CITES as being prohibited for international trade. However, these domestic and international laws are not well‐enforced within Iran, in light of the scale of open trade we observed. We recommend that authorities devote more time to monitoring these online platforms, and that resources are provided to in‐country enforcement efforts during the spring and summer, the observed peak of capture and trade. We also suggest that further research be conducted into the sources of wildlife, motivations for selling wildlife, and motivations for purchasing wildlife.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".