Trauma, Loss, Resilience, and Resistance in the Beauval Indian Residential School
Bibliographic record
Abstract
For over one hundred years, the Indian Residential School (IRS) system was used by the Canadian government to force assimilation on indigenous communities in what was later revealed to be a system rife with physical, psychological, and sexual abuse. This dissertation sought to examine a) how testimonies by former attendees of the IRS system reflect psychological understandings of trauma and loss, and b) how IRS attendees demonstrate resilience and resistance through testimony. Secondary analysis of pre-collected data was used to examine these questions. A thematic analysis was conducted of testimonies from 40 former attendees of the Beauval Indian Residential School that were given to the Truth and Reconciliation Commission in Canada in the province of Saskatchewan. Six overarching themes were identified: “Life before IRS,” “Conditions at IRS,” “Effects of IRS,” “Resistance,” “Resilience,” and “Healing.” A subset of themes was then given further attention to explore the depth of participant testimonies. Respondents presented a holistic understanding of the effects of trauma and loss on indigenous individuals, families, and communities, and demonstrated multiple forms of resilience and resistance to IRS. Rather than viewing the IRS experience as a series of traumatic events, this research suggests that it is more accurate to view the system as an exercise of colonial power, which attempted to accomplish its goal of forced assimilation using institutional conditioning reinforced by violence against indigenous children. Healing efforts should thus take a holistic approach, prioritizing reconnection to others, reconnection to culture, and promotion of survivor voices, to address the effects of IRS at multiple levels.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.025 | 0.022 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.004 |
| 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".