On proposing relational environmental metaphors to stimulate engagement and foster well-being in the midst of climate change
Bibliographic record
Abstract
Messages regarding climate change that are intended to stimulate responsible engagement can impact our mental health in both positive and negative ways, which in turn can increase or limit the potential engagement being sought through those very messages. Increasingly alarmist environmental metaphors are being brought into question due to their possibly detrimental impact on mental health and well-being, and in their place, relational environmental metaphors are proffered to instill hopeful and constructive individual and collective engagement for responsible climate action. This article discusses how both alarmist and relational environmental metaphors interact with eco-emotions. It proposes, in light of concepts arising from Porges' Polyvagal Theory - on the psychophysiology of autonomic states created in contexts of threatening cues and feelings of safety and connection -, that relational environmental metaphors are preferable for stimulating responsible collective engagement and fostering global well-being in the midst of climate change.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".