Bibliographic record
Abstract
When contemplating the history of Indigenous-settler relations in North America, it is important to consider whose stories are being privileged, and why. This paper will offer a comparative study of recent works by Canadian settler poet Laurie D. Graham and Oglala Lakota poet Layli Long Soldier, both of whom address stories of nineteenth century colonial violence against Indigenous people on either side of the Canada-US border. Both poets deal with similar Indigenous-settler dynamics relating to the government takeover of Indigenous lands, but they use different literary techniques to do so. In her poems “Battleford Gravesite” and “Visiting Pîhtokahanapiwiyin’s / Poundmaker’s Grave,” Graham writes about the 1885 Northwest Resistance in Saskatchewan, and the events which lead up to the hanging of eight Indigenous men, which was the largest mass hanging in Canadian history. Long Soldier writes in her poem “38” about the 1862 Sioux Uprising in Minnesota, and the eventual hanging of 38 men, which was the largest mass hanging in American history. Both of these poets question the memorialization of Indigenous history, however Long Soldier ultimately takes her process of remembering further than Graham by suggesting that memorialization should consist of both written words and embodied actions. By looking at these works together, I will investigate the role and value of memorialization of colonial history, and consider what poetry can offer in conversations about Indigenous history and reconciliation in North America.
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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 | 0.023 | 0.026 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".