Evaluating the effectiveness of live animal shows at delivering information to zoo audiences
Bibliographic record
Abstract
Live animal shows, which combine animal facts with trained behaviours, are commonly used to engage zoo visitors globally. However, such shows have been criticised for portraying a potentially unhelpful image of ‘performing animals’ and have raised issues of animal welfare ethics. Little is known about the educational effectiveness of these shows. Furthermore, the impact of ‘tricks’, used as attention-grabbing hooks, has received limited research attention. We evaluated the impact of a sea lion and a mixed species bird show on audience knowledge of animal facts. Over a quarter of zoo visitors attended some form of live animal show, demonstrating quantitatively that they are a major potential source of knowledge transfer. Show audiences were questioned immediately before (n = 299) or after (n = 265) each performance about relevant show content knowledge. Additionally, a general zoo visitor survey (n = 160) investigated post-visit knowledge recall. Audiences demonstrated significantly higher animal knowledge post-show compared to pre-show. Conservation action awareness showed weak positive change post-show. Audience education levels and weather conditions also had a weak positive effect on correct responses. However, animals performing trick-type behaviours were found to cause confusion regarding natural adaptations. We conclude that live animal shows should prioritise natural behaviours with a focus on conservation action.
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".