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Record W4229027353 · doi:10.3138/jcs-2020-0052

Settler Care: The Politics of Welcome (and Worry) in Canada’s “Most Racist City”

2022· article· en· W4229027353 on OpenAlexvenueaboutno aff
Krista Johnston

Bibliographic record

VenueJournal of Canadian Studies · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousPoliticsConceptualizationDominance (genetics)SociologyColonialismNarrativeRefugeeGender studiesPolitical scienceLawEcology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.698
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.025
GPT teacher head0.290
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2022
Admission routes2
Has abstractyes

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