Does more interpretation lead to greater outcomes? An assessment of the impacts of multiple layers of interpretation in a zoo context
Bibliographic record
Abstract
This study investigates the relationship between the level of exposure to interpretive media and the cognition, affect and behaviour of zoo visitors, i.e. what they report knowing, feeling and doing following their interpretive experience at the zoo. Visitors were surveyed at the exit to a particular zoo experience, a recently opened lion exhibit that uses an array of static and face-to-face interpretive media to convey messages about the difficulties faced by lions, particularly when they come into contact with humans. A validated self-report instrument consisting of 29 items was used to capture ten cognitive, affective and behavioural indicators or outcomes of the interpretation. The 288 respondents experienced between one and four different interpretive media, and the results on every one of the ten indicators reveal that visitors' reported cognitive, affective and behavioural outcomes were greater, many with statistical significance, as the number of interpretive media increased. The findings confirm and extend previous research which found that the cognitive impact of interpretation was not only greater with multiple layers of interpretation but also suggested the need for further research with other types of interpretive media on other visitors and in a wider range of sustainable tourism contexts.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".