Illegal Wildlife Trade in Traditional Markets, on Instagram and Facebook: Raptors as a Case Study
Bibliographic record
Abstract
Monitoring illegal wildlife trade and how the modus operandi of traders changes over time is of vital importance to mitigate the negative effects this trade can have on wild populations. We focused on the trade of birds of prey in Indonesia (2016–2021) in bird markets (12 markets, 194 visits), on Instagram (19 seller profiles) and on Facebook (11 open groups). We link species prevalence and asking prices to body size, abundance and geographic range. Smaller species were more traded in bird markets and less so online. Abundance in trade is in part linked to their abundance in the wild. Asking prices (mean of USD 87) are positively correlated with size and negatively with their abundance in the wild. Authorities seize birds of prey according to their observed abundance in trade, but only 10% of seizures lead to successful prosecutions. The trade is in violation of national laws and the terms and conditions of the online platforms; the low prosecution rate with minimal fines shows a lack of recognition of the urgency of the threat that trade poses to already imperilled wildlife. The shift of trade from physical bird markets to the online marketplace necessitates a different strategy both for monitoring and enforcement.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".