The role of a nature-based program in fostering multiple connections to nature
Bibliographic record
Abstract
Abstract Reconnecting to nature is imperative for the sustainability of humans on Earth, offering a leverage point for system change. Connections to nature have been conceptualized as a typology of five types as follows: material; experiential; cognitive; emotional; and, philosophical, ranging from relatively shallow to deeper connections, respectively. Educational programs that immerse individuals in nature have been designed to build an appreciation for places travelled, awareness of environmental issues and to promote pro-environmental behaviours. Using quantitative and qualitative data from 295 individuals who participated in National Outdoor Leadership School (NOLS) programs ranging from 14 to 90 days, we tested hypotheses to understand whether and to what extent NOLS influenced the five types of connections to nature. We further investigated whether deeper connection types were associated with greater intentions for pro-environmental behaviours. Findings showed that individuals generally reported greater connections to nature after the NOLS program, with emotional and material connections increasing the most. While intentions for pro-environmental behaviour increased from pre- to post-program, deeper connections to nature did not correspond to greater intention for pro-environmental behaviour. The strongest predictor of intention for pro-environmental behaviour was a cognitive connection, though an emotional connection was also a significant predictor. Ultimately, we found that the NOLS program fosters multiple connections to nature and increases intentions for pro-environmental behaviour. We call for more research to understand the relationships among connection to nature types and how those interactions may influence intentions for pro-environmental behaviour—in nature-based educational programs and in other 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 | 0.002 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".