Speaking of nature: Relationships between how people think about, connect with, and act to protect nature
Bibliographic record
Abstract
Human relationships with nature are increasingly being recognized as an important factor in environmental conservation. Understanding how people perceive and know nature, and the language they use to describe nature, their concepts of nature, could have important implications for conservation policy and management. This empirical research sought to examine and categorize concepts of nature, and explore how such thoughts relate to connection with nature and conservation behaviors. Multidimensional scaling revealed three concepts of nature categories: descriptive (e.g., plants, animals, landscapes), normative (e.g., conservation, balance, life), and experiential (e.g., activities in nature, positive emotions, aesthetic qualities), plus a complex category (two or more of the descriptive, normative, or experiential categories). Connection with nature scores (total and dimensions) were higher among participants who described nature in experiential or complex terms than those who described nature in descriptive terms. Participants who described nature in experiential terms were more likely to have participated in environmental volunteering, citizen science, picking up litter, and community gardening in the past year than those who used descriptive terms. Concepts of nature moderated the relationship between the connection with nature and picking up litter. These results may usefully inform conservation policies and campaigns intended to increase connection with nature and participation in conservation behaviors, through the use of language emphasizing experiential and more complex concepts of nature, by encouraging personal reflection on one's experiences of nature, and through the design of natural spaces that encourage active engagement with nature.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".