Are We Creatures of Logic or Emotions? Investigating the Role of Attitudes, Worldviews, Emotions, and Knowledge Gain From Environmental Interpretation on Behavioural Intentions of Park Visitors
Bibliographic record
Abstract
Environmental interpretation can improve sustainability by mitigating the negative impacts of nature-based recreation. However, we do not fully understand the psychological factors that influence interpretation’s efficacy in changing human behaviours. Specifically, the role of emotions has been understudied within environmental psychology and nature-based recreation. This study, therefore, provides further insight into the psychological processes driving pro-environmental behavioural intentions among overnight visitors attending personal interpretation programs in provincial parks in Alberta, Canada. In 2018 and 2019, we surveyed 763 attendees of personal interpretation events. We used latent variable structural regression modeling to test the hypothesized relationships between ecological worldview, attitudes, emotions, and pro-environmental behaviours. As predicted, there were positive relationships between worldviews, affective and cognitive attitudes, and emotions; these variables and knowledge gain were positively associated with pro-environmental behaviours. Findings suggest that interpretation should focus programming on the affective elements of communication, target personal meaning such as responsibility to act, and continue to transmit knowledge.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 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".