Combating the unsustainable exotic pet trade: Effects of conservation messaging on attitudes, demands, and civic intentions
Bibliographic record
Abstract
The exotic pet trade poses a major threat to biodiversity conservation. To combat biodiversity loss, it is essential to reduce demand for exotic pets and engage people in civic actions for wildlife conservation. Although messaging has been extensively used in conservation practice, little is known about how it can influence attitudes and various types of actions pertaining to the exotic pet trade. This study examined the impact of conservation messaging in the context of exotic pet ownership and wildlife entertainment visitation as common practices of the exotic pet trade. We randomly assigned participants in the U.S. to one of five messaging conditions: biodiversity loss and animal abuse (M1), zoonotic disease risks (M2), illegality (M3), social disapproval (M4), and neutral biological information as a control condition (M5). We found that all conservation messages (M1-M4) significantly decreased people’s favorable attitudes toward the exotic pet trade and their desire to visit wildlife entertainment. However, conservation messaging did not influence the desire for exotic pet ownership or intentions to take civic actions. Our findings highlight the potential of conservation messaging for attitude change and demand reduction for wildlife entertainment, but different approaches are necessary for promoting more effortful actions such as exotic pet ownership and civic actions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".