Of bison bones and fine China : a vegan approach to genocide on the Plains
Bibliographic record
Abstract
The digital prose-poem, “Bone China” (2015), by Canadian First Nations writer Paul Seesequasis responds to three historical photographs from the Saskatoon Public Library Archives (dated 1878, 1890, 1891) that depict towering stacks of bison bones, waiting to be shipped for industrial processing into products that included fine bone chinaware. Reading Seesequasis' poetic chinaware intersectionally, from an ethical vegan perspective, exposes the multiple metonymic significances of the late nineteenth-century bison Holocaust or “animal genocide” described by Anishinaabe theorist Gerald Vizenor. The mass slaughter of the bison not only brought Plains nations into submission to the US settler-colonial state but the physical elimination of Native presence (both human and other-than-human) worked to legitimize westward territorial expansion. Not even bones remained as the literal sign of prior occupation; settler pioneers, following the hunters and skinners, collected bison bones to sell for industrial purposes such as the production of bone china in the potteries of Staffordshire and elsewhere. This presentation contextualizes the intersectionality of Paul Seesequasis' poem via the human-animal discourses of films such as Dances with Wolves (1990) and Avatar (2009), and the video game Red Dead Redemption, to uncover the speciesist “human exceptionalism” that grounds the ideology of Manifest Destiny, the ongoing processes of settler colonization, and the commercial interests it continues to serve.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.031 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".