Does intensity of nature-based recreation drive environmental stewardship?
Bibliographic record
Abstract
While nature-based recreation is widely recognized for its well-being benefits, its role in fostering long-term environmental stewardship remains underexplored. This study bridges this gap by applying the Serious Leisure Perspective a framework that describes sustained, skill-intensive, and identity-building leisure participation, to examine how sustained engagement in outdoor activities cultivates ecological responsibility among recreationists in Gatineau Park, Canada. Using SmartPLS structural modeling, we analyzed survey data from 248 outdoor recreationists, assessing relationships between serious leisure (measured via the Serious Leisure Inventory and Measure) and environmental concern (using the New Ecological Paradigm Scale). Serious leisure significantly predicted environmental concern (R 2 = 0.563), with younger, educated participants showing heightened awareness. Subdimensions of environmental concern—anti-anthropocentrism (β = 0.329), balance of nature (β = 0.771), and ecological crisis (β = 0.766)—were strongly influenced by serious leisure engagement. Findings advance outdoor recreation research by demonstrating how serious leisure fosters place attachment and stewardship. We propose actionable strategies for park managers to design programs (e.g., skill-based workshops, citizen science) that leverage leisure engagement for sustainability outcomes. This study also can be used by park managers, environmental educators, and recreation planners looking for evidence-based strategies to encourage sustainable behaviors through leisure engagement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".