'More than biological': Cherie Dimaline's *The Marrow Thieves* as Countergenetic Fiction.
Bibliographic record
Abstract
This article reads Métis writer Cherie Dimaline’s novel The Marrow Thieves as one among a growing number of Indigenous counter-genetic fictions. Dimaline targets two initiatives that reductively define Indigeneity as residing in so-called Native American DNA: 1) direct-to-consumer genetic testing, through which an increasing number of people lay dubious claim to Indigenous ancestry, and 2) population genetics projects that seek urgently to sample Indigenous genetic diversity before Indigenous Peoples become too admixed, and therefore extinct. Dimaline unabashedly incorporates the terminology of genetics into her novel, but I argue that she does so in order ultimately to underscore that genetics is ill-equipped to understand Indigenous ways of articulating kinship and belonging. The novel carefully articulates the full complexity of Indigenous self-recognition practices, urging us to wrestle with the importance of both the biological (DNA, blood, relation) and the “more-than-biological” (Story, memory, reciprocal ties of obligation, language) for Indigenous self-recognition and continuity. To grasp Indigenous modes of self-recognition, the novel shows, is to understand that Indigenous belonging exceeds any superficial sense of connection that a DNA test may produce, and that, contrary to population geneticists’ claims, Indigenous Peoples are not vanishing, but instead actively engaged in everyday practices of survival. Finally, I point out that Dimaline – who identifies as Two-Spirit – does not idealise Indigenous communities and their ways of recognising their own; The Marrow Thieves also explicitly gestures to the ways in which Indigenous kinship-making practices themselves need to be rethought in order to be more inclusive of queer Indigenous Peoples.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".