The Role of Emotions in Generating and Sustaining Climate Action for Youth Climate Champions: An Exploratory Study in Northern Ontario
Bibliographic record
Abstract
Young people are frequently positioned at the forefront of the climate movement while also often being considered vulnerable to the mental and emotional impacts of climate change. While climate emotions research is rapidly increasing, little is known about young peoples’ lived emotional experiences of climate change and climate action. This research, grounded in social constructivism, aimed to explore the emotions, supports, and experiences that are generative and supportive of climate action for youth climate champions in Northern Ontario, Canada. In the spring of 2022, 12 youth climate champions (ages 15-24) participated in semi-structured interviews and asynchronous letter writing. This paper describes two thematic networks that were derived using thematic network analysis. The first network, climate emotions, is characterized by five organizing themes: 1) ecoanxiety, 2) emerging loss and grief, 3) triggers, 4) coping strategies, and 5) impacts on living and life. The second network, motivations and support for youth climate action, is described through three organizing themes: 1) action is unique to person and place, 2) intersections of emotion and action, and 3) key relationships and supports. Reflecting on this research, we offer two insights relevant to those engaging in health and education spaces with youth. First, there is a need to establish accessible, safe spaces for young people to collectively recognize, explore, and process complex climate emotions. Second, education systems across rural and remote regions are strategically positioned to support the health and wellbeing of young people through the implementation of holistic climate education that includes opportunities for engaging in collective climate action.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".