Illegal Wildlife Trade in a Tourism and Biodiversity Hotspot
Bibliographic record
Abstract
ABSTRACT There are clear connections between tourism, development, and sustainable use—particularly in biodiversity hotspots, where tourists may unknowingly purchase souvenirs made from protected wildlife. This issue is explicitly recognized in the Sustainable Development Goals, including SDG15, Life on Land, and SDG16, Peace, Justice, and Strong Institutions. On the island of Bali, a premier tourist destination with a strong local Hindu culture, we assessed the trade in legally protected wildlife (2022–2025) with the aim of improving the effectiveness of protected species regulations. We recorded 1440 animals for sale (849 as body parts and 591 alive). Almost half the species (27/59) were globally threatened, from other biodiverse hotspots, with few links to Balinese culture or society. Tourism on the island appears to have a negative impact on the environment, and this has implications for the sustainable development of Balinese society. We advocate for promoting sustainable tourism, embedded in Balinese culture, respecting local legislation and traditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".