What Can We Learn from Rural Youth in British Columbia, Canada? Environment and Climate Change—Issues and Solutions
Bibliographic record
Abstract
“What can we learn from rural youth?” was a youth-led arts-based participatory action research project carried out to understand and facilitate positive youth development in two rural communities in the province of British Columbia, Canada. Data was collected using photovoice, visual art, journal reflections, and group discussions. During the study, youth expressed a strong connection with nature for their development or wellbeing. Issues such as environmental degradation and climate change were identified as causes for concern. They discussed human responsibility for environmental stewardship both in their local communities and globally. Climate change hazards such as flood and fire, human action leading to environmental pollution, and human responsibility for environmental stewardship surfaced as issues for their development. Youth expressed a felt responsibility to act on climate change and to reduce the anthropogenic impact on the Earth. Based on youth voices, we conclude that attempts to engage youth in climate action without considering their psychosocial wellbeing, may overburden them.
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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".