A Sorry State of Affairs: Chinese Arrivants, Indigenous Hosts, and Settler Colonial Apologies
Bibliographic record
Abstract
We make and give gestures of apology every day, Canadians doubly so. Yet, grand acts of apology for more serious and sustained matters, such as historical and contemporary injustice against those with the least amount of social power, require far more ethical consideration and transformation than simply saying, “I am sorry.” Since the early 2000s, several political parties of the Canadian government have taken up the trend of making a spectacle out of national apologies to historically oppressed groups. Engaging with the concept of the settler colonial triad to theorize the histories of early Chinese arrivants’ experience, this work departs from the 2006 House of Commons apology made to Chinese Canadians on behalf of former PM Stephen Harper and explores the paradoxical operations behind state-sanctioned apologies, including the use of benevolence and hospitality as crisis management tactics resultant of Canada’s settler colonial configuration. Within this contradictory relation, those who identify as Chinese Canadian may find themselves questioning their belonging, given the historically- fraught social strategies used for the making of Canadian subjecthood. State-sanctioned apologies function to consolidate settler colonial reality and constitute a return to normalcy, which is why critical race scholars and scholars of settler colonial studies must look beyond unilateral relationships with the state.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.028 | 0.030 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".