From Walks to Acts of Kindness: Exploring the Connection Between Nature Relatedness and Prosocial Behavior
Bibliographic record
Abstract
This study examines the relationship between nature relatedness and prosocial behavior. Additionally, it addresses gaps in previous research, which are mainly correlational and inconsistently define both constructs. Nature relatedness is defined as either a trait or a state, while prosocial behavior is measured in various ways. To address these issues, this study manipulated state nature relatedness with Coughlan et al.’s (2022) guided imagery task and measured state nature relatedness (with the EINS), trait nature relatedness (with the NRQ), mood (with the PANAS), and prosocial behaviors across three measures: resource allocation (with the SVO), prosocial intent (with the PBIS), and a new Scenario-Based Prosociality Measure (SBPM). Results showed that the guided imagery task did not significantly increase state nature relatedness or affect mood. However, trait nature relatedness was positively correlated with state nature relatedness (r(156) = .45, p < .001), prosocial intent (ρ = .36, p < .001), scenario-based prosociality (ρ = .34, p < .001) and resource allocation (ρ = .16, p = .05). The new Scenario-Based Prosociality Measure (SBPM) demonstrated acceptable internal consistency (α = .69) and it was more strongly correlated with prosocial intent (ρ = .65, p < .001) than with resource allocation (ρ = .20, p = .01). These findings highlight trait nature relatedness’s stability and the need for more effective state nature relatedness manipulations.
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".