Understanding aggression and microaggressions by and against people of colour
Bibliographic record
Abstract
Abstract Efforts to understand racial microaggressions have focused on the impact on targets, but few studies have examined the motivations and characteristics of offenders, and none has examined microaggressions committed by members of racialized groups. The purpose of this study is to determine if racial microaggressions should be conceptualized as a form of aggression when committed by racialized individuals by examining the relationship between propensity to commit microaggressions and aggressive tendencies to help inform interventions. This nationwide survey recruited 356 Asian, Black and Hispanic American adults. Participants completed measures of likelihood of committing anti-Black microaggressions, aggression, negative affect, and ethnic identity. There was a significant negative correlation between ratings by diversity experts of microaggressive interactions being racist and participants’ likelihood of engaging in those same interactions. For each ethnoracial group, likelihood of committing anti-Black microaggressions was significantly positively correlated with all measures of aggression examined. The correlation between microaggressions and aggression was strongest for non-White Hispanic participants and weakest among Asian participants. A linear regression showed that aggression uniquely predicted microaggression likelihood, after controlling for respective co-variates within groups. Among non-White Hispanic participants, there was a significant positive correlation between negative affect and propensity to commit microaggressions, but this association disappeared in the regression analysis after accounting for aggression. A positive ethnic identity was not correlated with microaggression likelihood among Black participants. Findings indicate that microaggressions represent aggression on the part of offenders and constitute a form of behaviour that is generally socially unacceptable. Implications and cognitive behavioural treatment approaches are discussed. Key learning aims (1) People of colour generally recognize that racial microaggressions are unacceptable. (2) People of colour may commit microaggressions against other people of colour. (3) Anti-Black microaggressions are correlated to aggression in perpetrators. (4) Microaggressions are not solely attributable to negative affect or low ethnic identity. (5) Therapists should address microaggressions, even when committed by people of colour.
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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 | 0.001 | 0.003 |
| 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".