Trauma and Its Aftermath in Michelle Good’s <i>Five Little Indians</i>
Bibliographic record
Abstract
Residential schools for Indigenous children are one of the darkest chapters of Canadian history, and their effects are still felt to this day.Unfortunately, these traumatic effects on survivors are not discussed enough.Therefore, this paper analyzes Michelle Good's novel Five Little Indians (2020), and the plot which follows five characters and their lives after their stay at a residential school in British Columbia, the ensuing traumas, and their ways of coping in a world where they face daily oppression.The novel provides insight into different characters, and their points of view.This is a fruitful basis for analyzing different ways of coping with the traumatic past and the traumatizing present.The theoretical framework in this paper is based on trauma studies, mainly on works written by trauma theorists such as Judith Herman, Dori Laub, Shoshana Felman, and others.The paper hopefully contributes to a much needed discussion on the horrible mistreatment of Indigenous people in Canada, and sheds light on the lived-through trauma, as well as its aftermath.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.020 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".