Healing, Empowering, Engaging, Learning, and Decolonizing Through Culture: Living Wellness, Resilience, and Resurgence in the Classroom Through Creative Arts
Bibliographic record
Abstract
Art and Indigenous culture are inseparable. From the immaculately decorated lodges and war shirts of thousands of years to contemporary mixed and digital media images, Indigenous arts are expressions of survivance. Creative arts have sustained Indigenous ways of knowing, being, doing, and healing through attempted cultural genocide. Research has shown that art engages youth in life skill-building, learning, emotional regulating, and spiritual healing, supporting art as an intervention for wellness. Culturally-based artistic expression and the process of creating promotes wellness among Indigenous youth. As primary sites of assimilation and colonialism, educational institutions have a responsibility to enact reconciliation through culturally-rooted arts-based approaches to wellness. School psychologists are wellpositioned to support these approaches. This study took place in Kainaiwa in Southern Alberta and explored Niitsitapi artists’ and educators’ perspectives on the impacts of culturally-rooted arts-based interventions with Niitsitapi middle school students in the classroom. Over 2 days, professional Indigenous artists shared their art practices with students at a middle school in Kainai First Nation in Alberta. We had research conversations with 12 Niitsitapi community members involved in the event using a decolonizing, community-based approach. Indigenous storywork was used to understand research conversations, highlighting information and guidance for school psychologists to inform their engagement with Indigenous students and community members in schools. Findings emphasized art as healing, particularly given its connection to culture. Further, cultural engagement through art supports student wellness and educational engagement. Art can be used to empower voice, overcome deficit narratives, create new stories, and cope with disharmony. Art can also engage youth in discovery and learning, providing an alternative to a lecture style of learning, increasing enjoyment in the classroom experience. These findings have practical implications for future interventions and the integration of art pedagogically. This paper offers recommendations that highlight stark distinctions between culturally-rooted art practice and conventional Eurocentric art approaches in education.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".