Exploring Climate Emotions in Canada’s Provincial North
Bibliographic record
Abstract
The mental and emotional dimensions of climate change are increasingly concerning as extreme events become more frequent and severe, ecosystem destruction advances, and people become more aware of climate impacts and injustices. Research on climate emotions has rapidly advanced over the last decade with growing evidence illustrating that climate emotions can impact health, shape climate action, and ought to be considered in climate change communication, education, and engagement. This paper explores, describes, and discusses climate emotions in the context of Canada’s Provincial North: a vast region characterized by a vulnerability to climate change, remoteness, political marginalization, diverse Indigenous populations, and economies/livelihoods tied to resource extraction. Using postal survey data collected in two Provincial North communities (Thunder Bay, Ontario, and Prince George, British Columbia; N = 627), we aim to (1) describe climate emotions experienced in the context of Canada’s Provincial North, including relationships among specific emotions; and (2) examine if socio-demographic variables (gender, age, and parenthood) show a relationship with climate emotions. Results show high levels of emotional response to climate change overall, with worry and frustration as those emotions reported by the highest percentage of participants. We also find significant difference in climate emotions between men and women. A methodological result was noted in the usefulness of the Climate Emotion Scale (CES), which showed high reliability and high inter-item correlation. A notable limitation of our data is its’ underrepresentation of Indigenous peoples. The findings contribute to a greater understanding of climate emotions with relevance to similar settings characterized by marginalization, vulnerability to climate change, urban islands within vast rural and remote landscapes, and economies and social identities tied to resource extraction. We discuss our findings in relation to the literature and outline future research directions and implications.
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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 | 0.000 | 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) | 0.001 | 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".