Hey, Hey, Hey—Listen to What I Gotta Say: Songs Elevate Youth Voice in Alberta Wildfire Disaster Recovery
Bibliographic record
Abstract
Music pulses emotion in its lyrics, its tune, and in the creative process.A song can move people to dance, to reflect, and—often—to act. For an artist, a song’s creation can also reveal and clarify one’s own emotions. When people listen, a song can legitimize that the artists have something valuable to say—especially when the artists are youth who believe their ideas need a wider audience. This article talks about the power of song for youth recovery post-disaster in the context of the 2016 Fort McMurray wildfire disaster in Alberta, Canada. It highlights the use of music in a community-engaged research project that aimed to understand and amplify youth ideas for improving their community. The article draws on the value of Youth-Adult Partnerships, where eight youth worked with a professional recording studio in the wildfire-affected community to produce original songs for a youth-centric social media campaign. Focusing on the youths’ songs and personal experiences of their development, the article offers ways forward for wildfire recovery through processes that strengthen youth voice and wellbeing. The community-engaged research process underscores the power of music creation as an empowering method for enhancing youth engagement and reveals youths’ insights through their musical reflections on their priorities for a resilient community after disaster.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".