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Record W7080173799 · doi:10.25316/ir-20455

Inspiration for Healing and Reclaiming Cultural Wellness and the Spirit of Indigeneity in a Colonial “Canadian” Context

2025· dissertation· en· W7080173799 on OpenAlexaboutno aff

Bibliographic record

VenueVIUSpace (Vancouver Island University Library) · 2025
Typedissertation
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousColonialismContext (archaeology)DecolonizationNarrativePsychological resilience

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.889
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.198
Teacher spread0.190 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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