Leaching the Native American Hidden Dreams: Historical Oppression in the Necropolitical Dystopia of The Marrow Thieves
Bibliographic record
Abstract
Native Americans, as the original inhabitants of the Americas, have endured generations of systematic oppression stemming from European colonization. This historical oppression forms the core of Cherie Dimaline’s dystopian novel The Marrow Thieves (2017), written by the Canadian novelist of Métis descent. The novel is set in a near future where climate change and global warming have caused the deaths of millions, leaving the survivors traumatized and dreamless, stripping them of ambition. Only Native Americans retain the ability to dream, a gift passed down through their ancestors’ bone marrow. As a result, Indigenous peoples are hunted by both the American and Canadian governments, who seek to extract and exploit these dreams in an attempt to cure the widespread dreamlessness. This study aims to demonstrate how Dimaline anticipates the continued exploitation of Native Americans, even when there seems to be nothing left to take. To analyze this dystopian society, this employs Achille Mbembe’s concept of necropolitics—the politics of death. By engaging with the histories of Native Americans, dystopian studies, and necropolitics, this article argues that the fear of rising authoritarianism gives rise to a specific genre of fiction, which this paper terms “Necropolitical Dystopia.” This genre portrays future societies where life as we know it ends, as certain races assert their perceived superiority and oppress or annihilate others.
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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 | 0.021 | 0.028 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| 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".