Racial microaggressions in U.S. and Canadian contexts: Identity, perceptions of severity and the use of mindset signalling to repair harm
Bibliographic record
Abstract
Abstract Microaggressions are behaviours constituting indirect or unintentional discrimination, but little is known about how group identity affects perceptions of their harm. Canada and the United States have similar socio‐cultural backgrounds, but different socio‐political climates, with greater political polarisation and arguably stronger ties between politics and race in the United States (Pew research, 2020). Thus, the interplay between ethnic/racial identity (ERI), political identity, and perceived harm of microaggressions may differ across these countries. In a recent study of Canadians, perceived microaggression harm was associated with leftward political orientation rather than ERI. Here, we extend this work to a U.S. sample. In two experiments (N = 99; N = 210), White participants and Participants of Colour rated the severity of microaggressions and reported their political orientation and the strength of their ERI. Microaggression severity ratings were associated with left‐leaning political orientation, regardless of ERI. In Experiment 2, vignettes in which the perpetrator of a microaggression sought reparation by signalling a “reparatory open‐mindedness” reduced severity ratings compared to instances in which the source doubled down on the microaggression. Interestingly, the size of this reduction in perceived severity was smaller than for Canadian participants. Thus, perceived microaggression harm is governed by similar forces in Canada and the United States, but signalling mindset, while still effective, leads to smaller reductions in perceived harm in the United States. This could indicate differences in intergroup trust and polarization between these nations. This work underscores the role of political orientation in perceptions of microaggressions and highlights the efficacy of mindset signalling in mitigating their harm.
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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 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".