Climate emotions in remote, rural, and small communities across Canada: Exploring lived experiences through interviews and letters
Bibliographic record
Abstract
As the consequences of climate change become more severe and widespread, efforts to understand and address the emotional dimensions of the climate crisis are increasingly necessary. The aim of this study was to explore and describe the lived experiences of climate emotions in remote, rural, and small communities across Canada. Data were collected through semi-structured interviews and a letter writing process with 27 participants representing diversity in terms of geography, climate vulnerability, and socio-demographic characteristics. Thematic network analysis resulted in three global themes: (1) complex, intense, and interconnected climate emotions, (2) factors shaping climate emotions, and (3) consequences of climate emotions. The findings demonstrate that the lived experiences of climate emotions involve a wide array of complex, interconnected, and embodied emotions characterized by affective dilemmas and tensions. For most, climate emotions are challenging and experienced in isolation resulting in consequences for wellbeing, life decisions, and action. Importantly, the data illustrate the influence of intersecting identities, social factors, perceived responsibilities, and place in terms of giving rise and shape to climate emotions. The findings also emphasize that the lived experiences of climate emotions may be particularly impactful in remote, rural, and small communities that are commonly marginalized and disempowered, where people tend to have close connections to the natural world, and where a socialized silencing around climate change and climate emotions is pervasive. Taken together, the findings highlight the imperative of supporting collective coping through place-specific and intersectional processes that recognize the tensions and challenges that characterize climate emotions as well as the diversity of factors that give rise and shape to climate emotions.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.032 | 0.013 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".