Satisfied and Secured—An Integration of Self-Determination Theory and Attachment Theory in the Environmental Domain
Bibliographic record
Abstract
While environmental motivation research has investigated several factors that can facilitate and promote the adoption of pro-environmental behaviors, questions remain on how individuals can be brought to change their behaviors and habits. In the current study, we draw on attachment theory and self-determination theory to better understand why motivational interventions meant to increase pro-environmental behaviors are ineffective for some individuals. Using a person-centered approach, our analysis uncovered four latent profiles characterized by varying levels of attachment insecurity and basic psychological need satisfaction. Further analysis suggests that these four profiles are associated with distinct motivational pathways in the environmental domain. Our results suggest that self-determined motivation is a direct predictor of pro-environmental behaviors solely for individuals from the secure attachment and high-need satisfaction profile. This association was not observed in individuals arising from insecure attachment and low-need satisfaction profiles, suggesting that the association between motivation and pro-environmental behaviors commonly reported in the literature might be moderated by one's social environment. Implications for motivation researchers and policymakers are discussed, such as the relevance of considering attachment when designing motivational interventions in the environmental domain.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".