Developing a typology of climate grief expressions in Canada: a scoping review
Bibliographic record
Abstract
Abstract Increasing temperatures, severe storms, wildfires, and melting sea ice have made climate change a reality for Canadians. Climatic change can cause experiences of grieving and loss, often termed ‘climate grief.’ Our objective was to better understand emotions related to climate change by developing a typology illustrating the ways people in Canada express climate grief. In a scoping review (in English and French) of databases, popular media, social media, and art, we identified nine primary ‘mediums’ through which climate grief is discussed and expressed: (i) peer-reviewed research; (ii) grey literature; (iii) guiding frameworks; (iv) education; (v) social action gathering; (vi) mental health support; (vii) religions and spiritual practice; (viii) artistic expression; and (ix) media. Additionally, within those mediums we identified and categorized 26 forms of expression (secondary), and 40 types of expression (tertiary). Our review collected sources until 2022 and found that people in Canada express climate grief through diverse mediums, spanning Canadian provinces and territories. This typology can strengthen education, learning, and environmental decision-making to find new avenues to support the emotional and physical toll climate change can have on people in Canada.
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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 | 0.018 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.037 | 0.045 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".