From Zoo to Social Media: The Evolution of Human-Captive Wildlife Relations
Bibliographic record
Abstract
In the twenty-first century– an era of increasing domestic and international tourism- there are boundless opportunities to encounter wild animals both in their home countries and ex situ in zoological facilities around the world. Tourism activity– especially at accredited zoos and sanctuaries –plays a crucial role in the conservation of wild animal populations, and influences the welfare of individuals within involved species. Unfortunately, not all zoos and sanctuaries prioritize the conservation and welfare of their animals, such as those who promote irresponsible and mutually-harmful visitor-animal encounters for economic profit. While the relationship between visitors and animals at zoological facilities has shifted over time to match evolving morals and sentiments towards animals, there is still a storied tendency of visitors preferring close encounters with charismatic wild species. Since the 1970s, researchers’ attention has increasingly focused on assessing the influence of the visitor effect, which refers to the impact that viewing, touching, feeding, holding, and riding captive wildlife has on the animals. Many wildlife attractions promote such encounters, despite research suggesting that close interactions with visitors can cause stress and harm to involved species. Such activities are further promoted through the “selfie tourism” phenomenon, in which visitors capture images of themselves in too-close proximity to wild animals to be shared on social media. In this commentary, we consider the challenge of “selfie tourism”, and how it can promote unethical relationships between humans and wildlife and lead to deleterious implications for the animals’ conservation and welfare.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".