Seeing Nature Through a Spiritual Lens: An Experimental Test of the Effects of a Novel Photo-taking Task on Environmental Concern and Well-being
Bibliographic record
Abstract
Previous research has shown that people who report a greater spiritual connection with nature (“ecospirituality”) express a more highly moralized concern for its preservation. Other results also suggest a possible link between ecospirituality and subjective well-being. In order to test the causal nature of these relations, we created a novel intervention designed to temporarily boost ecospirituality and, in a high-powered preregistered study (N = 779), tested the effects of this intervention (compared to two control conditions) on measures assessing concern for the environment and well-being. Results on the effects of the ecospirituality intervention were inconclusive: Participants in all three conditions showed similar pre-intervention/post-intervention changes on the dependent measures, and also showed similar pre/post changes in self-reported ecospirituality (which served as a manipulation check). Exploratory correlational results showed that, across conditions, pre/post increases in self-reported ecospirituality predicted increases in both environmental concern and well-being. The correlational results replicate and extend prior findings—suggesting that ecospirituality may offer benefits to nature and to oneself—but additional research is required to establish causal evidence for this contention.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".