Inspiration for Healing and Reclaiming Cultural Wellness and the Spirit of Indigeneity in a Colonial “Canadian” Context
Bibliographic record
Abstract
Colonization has profoundly disrupted Indigenous people; it affects a person’s wellness, culture, traditions, governance, families, health, lands, and all one’s relations. While there is a plethora of research focused on decolonization, limited research features personal decolonization in contemporary life to achieve wellness and Indigeneity.Through Indigenous research methodology, exploratory narrative methods, and thematic analysis, the narratives from seven wellness experts and seven wellness role models uncovered four themes of the process of colonial trauma healing; 1) the Spirit of Indigeneity call to healing, 2) purging colonial poison, 3) reclaiming cultural wellness and the Spirit of Indigeneity, and 4) living cultural wellness guided by the Spirit of Indigeneity. The colonial context creates an inescapable environment that causes colonial trauma, which requires historical and ongoing healing; however, that does not mean Indigenous people cannot thrive or be well in the contemporary colonial world. They certainly can, and this research provides inspirational examples of how that is possible. This study illustrates the resilience of fourteen Indigenous individuals, and documents the reclamation and revitalization of their lives in a contemporary colonial context. In spite of the colonial context and ongoing colonization, Indigenous healing is possible and so is Indigenous thriving.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.027 | 0.037 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 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".