Youth Engagement in Climate Change Action: Case Study on Indigenous Youth at COP24
Bibliographic record
Abstract
While there are many studies about the environmental impacts of climate change in the Canadian north, the role of Indigenous youth in climate governance has been a lesser focus of inquiry. A popularized assumption in some literature is that youth have little to contribute to discussions on climate change and other aspects of land and resource management; such downplay of youth expertise and engagement may be contributing to climate anxiety (e.g., feelings of hopelessness), particularly in remote communities. Creating opportunities for youth to have a voice in global forums such as the United Nations Conference of Parties (COP24) on Climate Change may offset such anxiety. Building on previous research related to climate action, and the well-being of Indigenous youth, this paper shares the outcomes of research with Indigenous youth (along with family and teachers) from the Mackenzie River Basin who attended COP24 to determine the value of their experience. Key questions guiding these interviews included: How did youth impact others? and How did youth benefit from the experience? Key insights related to the value of a global experience; multiple youth presentations at COP24 were heard by hundreds of people who sought to learn more from youth about their experience of climate change. Additional insights were gathered about the importance of family and community (i.e., webs of support); social networks were seen as key to the success of youth who participated in the event and contributed to youth learning and leadership development.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| 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".