Racial Microaggressions: Critical Questions, State of the Science, and New Directions
Bibliographic record
Abstract
Racial microaggressions are an insidious form of racism with devastating mental-health outcomes, but the concept has not been embraced by all scholars. This article provides an overview of new scholarship on racial microaggressions from an array of diverse scholars in psychology, education, and philosophy, with a focus on new ways to define, conceptualize, and categorize racial microaggressions. Racism, along with its many forms and manifestations, is defined and clarified, drawing attention to the linkages between racial microaggressions and systemic racism. Importantly, the developmental entry points leading to the inception of racial bias in children are discussed. Theoretical issues are explored, including the measurement of intersectional microaggressions and the power dynamics underpinning arguments designed to discredit the nature of racial microaggressions. Also described are the very real harms caused by racial microaggressions, with new frameworks for measurement and intervention. These articles reorient the field to this pertinent and pervasive problem and pave the way for action-based responses and interventions. The next step in the research must be to develop interventions to remedy the harms caused by microaggressions on victims. Further, psychology must make a fervent effort to root out racism that prevents scholarship on these topics from advancing.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.006 | 0.019 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".