Re-Creation, Re-Membrance, and Resurgence: Richard Wagamese’s Indian Horse
Bibliographic record
Abstract
This article examines the novel Indian Horse (2012), written by Ojibwe Wabaseemoong Independent Nations member Richard Wagamese (1955-2017) at the height of the Truth and Reconciliation Commission era. Wagamese finds inspiration in the testimonies and experiences of hundreds of victims of Canada’s residential school system, including those of his own family members. The article contextualizes the novel in the Truth and Reconciliation Commission era and explores Saul’s narrative journey to recover his suppressed memories of personal and collective abuse at St. Jerome’s Indian Residential School through the lens of Indigenous resurgence and grounded normativity. Thus, the paper draws on Michi Saagiig scholar Leanne Betasamosake Simpson’s writings on Indigenous radical resurgence to explore the retrieval of Indigenous ways of existing in the world as the way towards decolonization and Indigenous sovereignty. The paper argues that Saul is able to overcome his trauma-induced amnesia, born from the necessity to endure and adapt, and to escape the spiral of shame, isolation, and self-destruction in which he engages only after he embraces discursive Indigenous ways of healing. Wagamese therefore constructs a narrative in which the protagonist’s development mirrors the ideal that the author sets for Canada, in which reconciliation with Indigenous truth will not take place unless the whole story is acknowledged.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.023 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".