Settler Care: The Politics of Welcome (and Worry) in Canada’s “Most Racist City”
Bibliographic record
Abstract
Care and welcome are central facets of Canadian national mythology. In this paper, I analyze expressions of care in news media coverage of the arrival of Syrian refugees to the city of Winnipeg beginning in 2015. Discussions about who is deserving of care, about what kinds of care should be extended, and about the apportioning of care between refugees and Indigenous peoples all demonstrate that in this instance, discourses about care serve to normalize and perpetuate settler colonialism. Delving into these narratives, this paper develops a conceptualization of settler care, a hollow expression of care for others which serves to bolster settler dominance and to maintain the subordinated positions of refugees and Indigenous peoples within the settler colonial order. I identify two sets of narratives within settler care: settler welcome and settler worry. Each takes several different forms, but in every iteration expressions of settler care serve to normalize settler dominance. When the belonging of refugees is juxtaposed with that of Indigenous peoples, neither is afforded dominance or centrality in the settler order, and Indigenous practices of welcome and demands for justice are subordinated to the settler project. In contrast to these expressions of settler care, the final section of the paper examines anti-colonial practices of care and welcome and reflects on the possibilities and challenges of alternative relations of care.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.061 | 0.042 |
| Scholarly communication | 0.014 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| 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".