Alternative Food Practices as Pathways to Cope with Climate Distress
Bibliographic record
Abstract
Experiences of distress and challenging emotions in response to the climate crisis are increasingly common, particularly among young adults. These experiences can include challenging emotions caused by the harmful environmental impacts of conventional food systems, as their contributions to greenhouse gas emissions have become more widely known. While recent studies have examined various experiences of climate distress, the interaction between climate distress and food practice remains poorly understood. In this research, we turn to this intersection by examining the experiences of climate distress of young adults who have alternative food practices, and the interaction between their climate distress and their alternative food practices. Guided by an exploratory, single case study research approach, this research draws from 20 semi-structured interviews conducted with young adults located in urban centres in the Southeastern Prairie Region of Canada. Thematic analysis of the findings reveals that participants experience a variety of climate emotions and a personal responsibility to act in response to the climate crisis. The findings suggest that because of their ability to facilitate a meaningful and practical environmental impact, alternative food practices represent significant climate actions and may be pathways to facilitate coping or managing climate distress among young adults. Results demonstrate the psychological impacts of the climate crisis on young adults, highlighting the need for action on climate change and climate distress. Increasing the accessibility of alternative food practices may support young adults in coping with challenging 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| 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".