Beyond trauma: Decolonizing understandings of loss and healing in the Indian Residential School system of Canada
Bibliographic record
Abstract
Abstract Indigenous scholars and others who study the experiences of Indigenous communities have long criticized the psychocentric approach to trauma held by most clinical professionals. A recent example of this was the Canadian government's reparations for Indian Residential School system (IRS) survivors, which focused largely on individual psychological harms rather than broader effects of colonial oppression. Beginning in 1867 and continuing throughout the latter half of the twentieth century, Indigenous children were routinely removed from their home communities in Canada and placed into the IRS system, where they were frequently subject to physical, psychological, and sexual abuse. Using thematic analysis, this study draws on survivor testimonies from one residential school to explore how their descriptions of the effects and healing from IRS abuses differ from psychocentric understandings of trauma and loss. Results indicate that survivors describe IRS effects in sociocentric, ecocentric, and cosmocentric terms more so than psychocentric ones and place deep importance on healing through connection to family, culture, and community. To decolonize itself as a discipline and better serve Indigenous communities, the field of psychology must open up to understandings of trauma, loss, and healing that decentralize the individual, a difficult task given the discipline's psychocentric nature.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.042 | 0.050 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| 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".