Exotic pet trade in Canada: The influence of social media on public sentiment and behaviour
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The live trade in wild animals can increase the risk of escape of exotic animals, introduce invasive species, spread zoonotic diseases, over-exploit wild populations, and harm animal welfare. Trade in exotic pets is a particularly understudied issue in Canada. While Canadians generally have pro-environmental attitudes, it is unclear whether this extends to the trade in exotic animals. With most Canadians on social media, we aimed to use Natural Language Processing of social data to examine public sentiment towards exotic pet trade in Canada. We analysed 9,274 posts on Twitter (now 'X') about exotic pets between 2012 and 2022, and 150,236 comments from 2568 TikTok videos showing exotic pets from 50 unique Canadian accounts. We found that social media users demonstrate markedly positive attitudes towards the live trade in reptiles and amphibians, mammals, birds, and arachnids and insects, even on TikTok videos showing poor animal care and questionable legality. We propose a conceptual framework for how exotic pet influencers directly and indirectly contribute to increased demand for exotic pets through opinion leadership, sharing information on where to buy exotic pets, and normalising exotic pet ownership. We suggest that it is important to raise public awareness among social media users about the challenges associated with wildlife trade, including animal welfare considerations, and the links between exotic pet trade and conservation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it