Institutional Racism and Racial Politics in Richard Wagamese’s Indian Horse: A Critical Race Study
Bibliographic record
Abstract
This paper investigates Indian Horse (2012) by Richard Wagamese using Critical Race Theory (CRT) as a theoretical framework, particularly drawing on the concepts introduced by Richard Delgado and Jean Stefancic’s jointly written work Critical Race Theory: An Introduction (2001). It follows Saul Indian Horse, a First Nations man, whose life is shaped by the traumatic legacies of Canada's residential school system and the systemic racism that dominated both his personal experiences and broader societal structures. This study examines how the narrative not only represents the lived realities of Indigenous peoples in Canada but also critiques the racial hierarchies and race-based social structures. Through Saul’s journey, the novel reveals how the systemic marginalization of Indigenous peoples is perpetuated through cultural erasure, physical abuse, and social exclusion. The residential school system, depicted as both a symbol and mechanism of colonialism, operates as a microcosm of broader societal racism. This paper also explores the theme of healing and the narrative's challenge to dominant historical narratives, illustrating the ways in which personal resilience intersects with collective struggles for justice. In applying Delgado and Stefancic’s framework, the paper argues that Indian Horse functions not only as a literary critique of colonialism but also as a call to action for dismantling the racialized structures that continue to oppress Indigenous communities. The novel underscores the importance of storytelling in countering hegemonic narratives, emphasizing the transformative potential of Indigenous voices in shaping a more equitable future.
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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 | 0.001 |
| Science and technology studies | 0.030 | 0.031 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| 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".