Journey to Churchill interpretative exhibit case study: Innovation in evaulation
Bibliographic record
Abstract
This chapter demonstrates the importance of innovative evaluation methods in visitor contexts, through a case study at a zoo. The Assiniboine Park Zoo in Winnipeg, Manitoba developed a new exhibit, Journey to Churchill. This exhibit was intended to help visitors connect with and learn about arctic animals, ecosystems, conservation and climate change. To assess whether outcomes were achieved, three different methods were used. Readers will learn about personal meaning mapping, overheard conversations, and social media analysis as effective methods for evaluating a range of visitor outcomes. Particularly, this research demonstrates that innovative and flexible methods are needed to assess a broad range of visitor outcomes such as interpretive learning, emotional connection, behaviour changes, and understanding public discourse that may not be possible with traditional survey or interview methods. The real-life impacts of this case study are discussed to demonstrate the importance of visitor evaluation for effective program planning, review, and ongoing guidance in the management of visitor experiences. By the end of this case study readers will be able to: 1) demonstrate an understanding of the importance of evaluation in visitor contexts; 2) identify three innovative methods that can be used in visitor evaluations; 3) and demonstrate an understanding of leisure experiences as potential opportunities for free-choice learning, emotional connections, and sustainable behaviour change. In general, this case study found that by using this combination of research methods that the interpretive, emotional, and behavioural goals were mostly achieved by the exhibit, but that there was a lack of public awareness about research and conservation efforts facilitated by the APZ. Additionally, this case study demonstrated that the JTC exhibit can facilitate meaningful learning about Arctic animals and climate change through emotional connections to the animals in the exhibit, especially the polar bears.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".