An Indian Residential School survivor's journey with Truth and Reconciliation
Bibliographic record
Abstract
As an Indigenous woman and an Indian Residential School survivor, I embark on a journey that forms a roadmap of my life experiences that are part of the history of Canada. I share who I am and how I self-identify as someone born of mixed ancestry. I share stories of my Dene culture and the experience that my siblings and I had while at Indian Residential School (IRS). The question, “What is my truth and reconciliation?” opens up an array of interpretations and subsequent questions that reference many aspects of my political and cultural viewpoints. I use a blended qualitative research approach using Indigenous research methodology and autoethnography to explore Indigenous cultural ideologies to interpret reconciliation. Data was collected through a set of questions in a guided focus group with my two siblings. The focus group questions brought out thoughts, feelings, and emotions from IRS that resonated around childhood trauma. Through thematic analysis, I discovered similarities in our answers and together we gained a deeper understanding of our childhood trauma as experienced at IRS. I bridge the gap between the past and the present as I acknowledge my lived realities that enable me to move beyond personal trauma to healing in the form of decolonization and reconciliation. In my Indigenous world today those two words serve as a bridge towards my healing journey. \nKeywords: healing, trauma, reconciliation, decolonization, separation, memory, survivor
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.037 | 0.018 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".