Bibliographic record
Abstract
In past research, nature connectedness has been associated with higher levels of psychological health (Howell, Dopko, Passmore, & Buro, 2011), and meaning in life (Howell, Passmore, & Buro, 2013). Eco-anxiety, or the experience of anxiety in relation to global climate change, has not been studied previously in relation to nature connectedness, meaning in life, or psychological well-being. Moreover, no prior research has examined implicit theories of environmentally responsible behaviour (ERB); that is, fixed and growth mindsets regarding one’s ability to engage in ERB. In an ongoing study with undergraduate participants, we hypothesized that nature connectedness and fixed mindsets regarding ERB would predict eco-anxiety which, in turn, would predict low meaning in life and low well-being. Results showed a strong positive correlation between eco-anxiety and total nature connectedness. When correlated with the nature connectedness subscales, eco-anxiety was significantly associated with the self and perspective subscales, but not the experience subscale. In addition, eco-anxiety did not significantly correlate with the remaining variables. Implications of the findings are discussed. Faculty Mentor: Andrew Howell Department: Psychology
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".