Thinking Like a Park: The Effects of Sense of Place, Perspective-Taking, and Empathy on Pro-Environmental Intentions
Bibliographic record
Abstract
Over the past few years there have been various calls for reformulating sustainable development in a more local and relational manner. Based on Aldo Leopold’s description of his experience in “Thinking Like a Mountain” as well as concepts in recreation and psychology, a framework was developed that examined potential relationships among sense of place, perspective-taking, empathy, and pro-environmental intentions. In order to determine if these relationships were consistent with study expectations, 258 visitors to a Canadian national park completed an on-site questionnaire. As expected, sense of place did significantly affect both empathy and perspective-taking, and perspective-taking did significantly affect empathy. Furthermore, although neither sense of place nor empathy affected the self-focused depreciative intention (e.g., not littering), sense of place did significantly affect the place-related intention (e.g., not visiting a favorite place for environmental reasons) indirectly through empathy, and both empathy and sense of place significantly affect the other-focused depreciative intention (e.g., picking up other peoples’ litter), the poaching reduction intention (e.g., paying higher entrance fees), and the volunteering intention (e.g., working on park projects). Study findings, management implications, and future research recommendations are discussed.
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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 | 0.011 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".