From mother to Mother Nature: The role of interpersonal attachment in human–nature relations
Bibliographic record
Abstract
Over the past two decades, environmental psychologists have become increasingly interested in the concept of nature connectedness: the degree to which people feel connected to the natural world.Nature connectedness has been shown to predict a variety of important individual differences, including stronger proenvironmental attitudes and behaviours, and greater well-being.Despite the increased attention that this topic has recently received, there is a dearth of research that has comprehensively examined how people's relationship to nature might be related to and influenced by social relationships and contexts.In this dissertation, I consider and test the applicability of attachment theory to human-nature relations.Across two studies, I examined whether interpersonal attachment orientations (i.e., attachment anxiety and attachment avoidance) are associated with nature connectedness at the trait-level.I also investigated whether momentary activation of specific attachment-related schemas influence people's motivation to connect with nature, and whether dispositional attachment orientations and nature connectedness moderate the impact of these temporarily activated schemas on motivation to connect with nature.Evidence for relationships between interpersonal attachment orientations and nature connectedness at the trait-level were weak and inconsistent across the two studies.Motivation to connect with nature did not differ across experimental conditions in either study, but some evidence was found for interpersonal attachment orientations moderating the effect of some of the experimental conditions.In general, the current research did not provide clear and consistent evidence for a link between how people relate to close others and how they relate to nature, or for attachment-related contexts impacting people's motivation to connect with nature.Based on these results, one could conclude that attachment theory appears to have limited relevance for understanding human-nature relations.Limitations and future research directions 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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".