Biodiversity Exploitation for Online Entertainment
Bibliographic record
Abstract
Anthropogenic wildlife exploitation threatens biodiversity worldwide. With the emergence of online trading which facilitates the physical movement of wildlife across countries and continents, wildlife conservation is more challenging than ever. One form of wildlife exploitation involves no physical movement of organisms, presenting new challenges. It consists of hunting and fishing “experiments” for monetized online entertainment. Here we analyze >200 online videos of these so-called experiments in the world's largest video platform (YouTube). These videos generated about half a billion views between 2019 and 2020. The number of target species (including threatened animals), videos, and views increased rapidly during this period. The material used in these experiments raises serious ethical questions about animal welfare and the normalization of violence to animals on the Internet. The emergence of this phenomenon highlights the need for online restriction of this type of content to limit the spread of animal cruelty and the damage to global biodiversity. It also sheds light on some conservation gaps in the virtual sphere of the Internet which offers biodiversity-related business models that has the potential to spread globally.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.046 | 0.004 |
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".