Racial microaggressions: Identifying factors affecting perceived severity and exploring strategies to reduce harm
Bibliographic record
Abstract
Abstract Microaggressions are speech or actions constituting indirect, subtle, or unintentional acts of discrimination, and awareness of their harmful effects has grown in recent years. Increased awareness could improve inter‐group interactions, but also poses challenges. Fear of misspeaking, or fear of being subject to microaggressions can stifle interactions. We investigated how people from different racial and ethnic groups and political orientations judge the severity of various forms of racial microaggressions, and we tested a specific strategy to mitigate the harm of racial microaggressions. Specifically, in Experiment 1, White participants (WP) and participants of colour (POC) rated the severity of various microaggressions (depicted in vignettes). Participants also reported their political orientation and strength of racial/ethnic identity. Regardless of racial/ethnic group, left‐leaning political orientation was associated with higher perceived severity of racial microaggressions. Furthermore, severity ratings from POC were higher for those who identified more strongly with their ethnic/racial group. In Experiment 2, we again obtained severity ratings, but we used microaggression vignettes that were manipulated to reveal the source s mindset as either reparatory and open‐minded (ROM), or not. Critically, severity ratings were significantly lower for vignettes in which ROM was messaged. The importance of these results is twofold. First, they reveal that political orientation can override other factors like racial group membership when judging the severity of racial microaggressions, and second, they show that augmenting problematic speech with information about mindset, can mitigate perceived harm. Overall, this work contributes to a richer understanding of microaggressions, and has implications for theory and practice.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".