Association of climate anger with loneliness and social isolation among the general adult population in Germany
Bibliographic record
Abstract
Abstract Aim Our aim was to investigate the association of climate anger with loneliness and perceived social isolation. Subject and methods We used data from the general adult population in Germany ranging from 18 to 74 years. Data were collected in January 2025. Loneliness was quantified using the De Jong Gierveld instrument and perceived social isolation was assessed using the Bude and Lantermann tool. Climate anger was measured using an extended subscale of the Inventory of Climate Emotions (ICE). Results Our sample consisted of 3270 adults from Germany (mean age 47.0 years [SD 15.3]; 50.4% female). Adjusting for a wide array of covariates, regressions showed that climate anger was significantly associated with perceived social isolation among the total sample ( β = .05, p < .001) and men ( β = .08, p < .001) but not women. Moreover, this association was significant among younger individuals aged 18 to 29 years ( β = .10, p < .01) and older adults aged 55 to 74 years ( β = .06, p < .01), whereas it was not significant among middle-aged individuals aged 30 to 54 years. It is worth noting that climate anger was not associated with loneliness in the total sample and all subgroups. Conclusion We add the very first evidence regarding the association between climate anger and social isolation (particularly among men and younger individuals). One may conclude that climate change is a social challenge that could potentially lead to a split in society. Future research is urgently required to further examine such associations.
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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 | 0.005 | 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".