From childhood blue space exposure to adult environmentalism: The role of nature connectedness and nature contact
Bibliographic record
Abstract
Nature contact in childhood is associated with pro-environmental behaviours (PEB) later in life. While previous literature focused on nature contact in general, the current work specifically explored childhood blue space exposure (coasts, rivers, lakes etc.) and potential mechanisms underlying any relationship with PEBs in adulthood. Cross-sectional data from an Austrian sample representative on age, gender, and region (N = 2,370) were used to test a serial-parallel mediation model linking recalled childhood blue space exposure to self-reported adult PEBs via, first, nature connectedness and, second, recent visits to green and blue spaces. Results supported significant serial mediation, with recalled childhood blue space exposure linked to nature connectedness in adulthood, which was in turn associated with more frequent recent visits to green and blue spaces, which in turn predicted PEB. Significant direct and indirect effects were observed, while controlling for known individualand area-level covariates. Findings highlight the potential importance of childhood blue space exposure as well as life-long nature contact for improving nature connectedness and PEB and add to calls for protecting and maintaining natural water bodies and to improve their safety, as spending time around them in childhood may play a role in fostering PEB and ultimately improving planetary health.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".