Richard Wagemese's Indian Horse: Stolen Memories and Recovered Histories
Bibliographic record
Abstract
Este artículo pone de manifiesto las estrategias narrativas mediante las cuales el autor anishinaabe canadiense Richard Wagamese somete al lector de su novela Indian Horse (2012) a la misma violencia sufrida por el joven héroe cuando la repentina resurgencia del recuerdo traumático reprimido acaba rompiendo la linealidad aparente de su historia. Asimismo, este estudio pretende demostrar que la reapropiación de la memoria robada, y por la tanto la posibilidad de reconstruirse tras el traumatismo vivido, pasan por una reapropiación de las formas aborígenes del relato gracias a las que Wagamese contribuye de manera significativa a la reescritura de la historia de las escuelas residenciales autóctonas de Canadá. This paper purports to explore the narrative devices which enable the Anishinaabe Canadian author Richard Wagamese to compel the reader of his novel Indian Horse (2012) to experience the same violence as that faced by the young protagonist when the repressed memory of the terrible abuse suffered at an Indian residential school resurfaces decades after, disrupting the apparently linear course of the story. This study also seeks to show that Wagamese offers a major contribution to the rewriting of the history of residential schools in Canada by reclaiming Aboriginal narrative forms as a means to recover stolen memories, and thus to reconstruct both the fragmented (his)story and the shattered self.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.022 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".