Is social media the new wet market? Social media platforms facilitate the online sale of bushmeat in West Africa
Bibliographic record
Abstract
Social media provides a platform for wildlife crime syndicates to access a global consumer-driven market. Whilst studies have uncovered the online trade in wildlife, the availability of wild meat (bushmeat) has not been assessed. To investigate the sale of wild meat online, we analysed 563 posts published between 2018 and 2022 from six West African Facebook pages selected using predetermined search criteria. Across 1511 images and 18 videos, we visually identified 25 bushmeat species-level taxa including mammals (six Rodentia, five Artiodactyla, three Carnivora, two Pholidota, one Primate, two Lagomorpha, one Hyracoidea), birds (three Galliformes) and reptiles (two Squamata), predominately advertised as smoked (63%) or fresh (30%) whole carcasses or portions. Among the species identified, 16% feature a status of concern on the International Union for Conservation of Nature (IUCN) Red List (Near Threatened to Endangered), 16% are listed on the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES; Appendix I and II) and 24% are either fully or partially protected by local legislation. Images were commonly used as propaganda rather than to display inventory, where additional taxa protected from game hunting in West Africa, such as hornbill, were exclusively listed in captions. The advertisement of these protected and vulnerable species on the surface web indicates weak local and international legislative enforcement. Comparatively, when the same search criteria were applied to the deep web browser Tor no results were generated, reinforcing the idea that bushmeat vendors have no need to hide their activities online. Despite local and international trade restrictions, the taxa advertised feature similarities with bushmeat seizures reported in Europe, alluding to the interconnectedness of the trade facilitated by social media. We conclude that enhanced policy enforcement is essential to combat the online sale of bushmeat and mitigate the potential biodiversity and public health impacts.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".