The Continuum of Eco-Anxiety Responses: A Preliminary Investigation of Its Nomological Network
Bibliographic record
Abstract
In the face of mounting environmental issues, people around the world are reporting the experience of difficult emotions such as anxiety and worry, or what is increasingly referred to as eco-anxiety. It is often acknowledged that symptoms of eco-anxiety can range in severity or fall along a continuum. Such a proposition has important implications, as it may help to explain why some forms of eco-anxiety are more mal(adaptive) than others. In five studies (Total N = 2939) across three countries (Canada, China, United Kingdom), we examined how measures that may encompass a continuum of environment-related worry and anxiety were associated with each other and with measures of environmental concern, an older concept that may capture the less severe end of eco-anxiety responses. We also explored if these various measures were differentially linked to aspects of mental health and a pro-environmental orientation. Results revealed that measures of eco-anxiety and environmental concern were often moderately-strongly correlated. Eco-anxiety measures exhibited relatively consistent relationships with greater ill-being but mixed relationships with indices of well-being. There was some evidence of more severe eco-anxiety measures being associated with poorer mental health and environmental concern measures being associated with better mental health. Measures of both eco-anxiety and environmental concern evidenced larger and more consistent relationships with indices of a pro-environmental orientation, with the most severe eco-anxiety measure exhibiting some notably weaker relationships. Together, the present work provides preliminary insights into the nomological network of the continuum of eco-anxiety responses and its integration into future work on eco-anxiety.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".