How Happy Do These Animals Look? Exploring Factors Influencing Children’s Perceptions of Animal Welfare at the Zoo
Bibliographic record
Abstract
Understanding how children and adolescents perceive zoo animal welfare provides insights into public views on captive conditions. This study used a mixed-methods approach to investigate these perceptions. A total of 254 participants (aged 7–18) were surveyed, with 113 completing the full circuit, which included species from distinct phylogenetic groups: monkey, macaw, caiman, frog, fish, and tarantula. Qualitative analysis (n = 254) evaluated children’s recognition of animal needs, whereas quantitative analysis (n = 113) examined how phylogenetic distance, participants’ emotional state, and overall welfare perception influenced animal and environmental assessments—a structured survey evaluating perceived animal health, enclosure conditions, and behavioral expression. Participants’ understanding of animal welfare extended beyond basic needs, recognizing the importance of species-specific behaviors and appropriate environments. Environmental and animal assessment scores were significantly influenced by species, participants’ emotional states, and overall welfare perceptions: fish received the highest scores, positive emotions were associated with higher ratings, and a better overall welfare perception correlated with more favorable assessments. We inferred that phylogenetic distance, emotional state, and general welfare impressions shaped how children and adolescents evaluate animal welfare. The study supported zoo-based educational strategies and reinforced the role of zoos in promoting welfare awareness and conservation-oriented attitudes.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".