Intergenerational trauma and stories of healing through Jesus
Bibliographic record
Abstract
Through a storytelling/yarning methodology (Bessarab & Ng'andu, 2010) and experience centered narrative research (Patterson, 2008), three Indigenous followers of Jesus and original inhabitants of the lands currently known as Canada, shared their stories of healing. The storytelling/ yarning method (Bessarab & Ng'andu, 2010) is rooted in Indigenous ways of knowing and fit seamlessly with the participants diverse Indigenous backgrounds and shared oral traditions. Through the experience centered research model, each participant engaged in meaning making of their personal narratives, reconstructed and presented their stories as their human lived experience, and finally, revealed their metamorphosis (Patterson, 2008) and contributions to Indigenous knowledges. The experience centered research framework utilized for knowledge gathering worked concertedly with the storytelling/yarning methodology as the healing stories presented here evolved not as stories of defeat, but of strength (Bessarab & Ng'andu, 2010). Some key teachings and themes arising from their stories include trauma, forgiveness, resilience, family, healing, and hope. This study aims to reveal Indigenous stories of healing and cease the perpetuation of harm to Indigenous peoples who have declared Jesus as their source of healing. Furthermore, this study aims to situate the knowledges gathered through these healing stories within the academic body of Indigenous knowledges.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.020 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".