Grief, Tradition, and Settler Colonialism: Recommending Karen McBride’s Crow Winter
Bibliographic record
Abstract
Presenter: Daniel Green, English Language and Literature Faculty Supporter: Professor Petra Fachinger. Students reading Indigenous texts is (or should be) of increasing priority in schools across Canada. As we look for Indigenous narratives to assign, “accessibility” for young, non-Indigenous students, should be considered, as well as what will allow for a non-didactic learning experience. My presentation focuses on why Karen McBride’s Crow Winter would be the optimal text to recommend to a teenage relative or friend. While plenty of other texts explore certain aspects of Indigenous lived experience from a more explicitly anti-racist perspective, Crow Winter will teach students about Algonquin Anishinaabe culture as well as life in general. Overall, McBride’s linguistic talent and her engagement with grief, loss, and death is riveting and universally comprehensible, thereby making it most accessible largely because of its focus on said universal themes. The novel does not aim to push an agenda, or explicitly educate about Indigenous Peoples—the learning experience and educational aspects of Crow Winter are seamless. By the end of the novel, I found that I had learned more about Anishinaabe tradition and ceremony from Crow Winter than from any other text. I would recommend Karen McBride’s Crow Winter to a teenager because of her use of Nanabush, the compelling portrayal of the effects of settler colonialism, and her powerful expression of grief.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.016 | 0.011 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.008 |
| 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".