Insights for Building Community Resilience from Prioritizing Youth in Environmental Change Research
Bibliographic record
Abstract
Youths are the next generation to foster community resilience in social–ecological systems. Yet, we have limited evidence on how to engage them effectively in learning about environmental change. One opportunity includes the involvement of youths in research that connects them with older generations who can share their values, experiences, and knowledge related to change. In this community-based study, we designed, assessed, and shared insights from two intergenerational engagement and learning interventions that involved youths in different phases of research in the Saskatchewan River Delta, Canada. For Intervention 1, we involved students as researchers who conducted video and audio recorded interviews with adults, including Elders, during a local festival. For Intervention 2, we involved students as research participants who reflected on audio and video clips that represented data collected in Intervention 1. We found that Intervention 1 was more effective because it connected youths directly with older generations in methods that accommodated creativity for youths and leveraged technology. Engaging the youths as researchers appears to be more effective than involving them as research participants.
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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 | 0.017 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".