Racial Microaggressions: Empirical Research that Documents Targets’ Experiences
Bibliographic record
Abstract
Racism is mostly associated with overt and intended harm.But it also features subtle racist interactions that affect targets' mental health, internal experiences, and social as well as professional lives.These subtle forms of racism often remain invisible or are dismissed as trivial.We employ the concept of racial microaggressions to analytically conceptualize the impact of everyday racial acts -whether they are frequent or cumulative, verbal or nonverbal indignities, intentional or unintentional -within a conceptual framework in psychological and educational research.In this chapter, we define the term racial microaggressions and offer four superordinate categories of the concept that we use to organize themes from prior qualitative research (Houshmand, Spanierman, & De Stefano 2017;Sue & Spanierman 2020).Next, we describe various instruments researchers have developed to measure experiences with racial microaggressions (i.e., frequency and distress levels).We then describe a growing body of research that provides evidence of the harmful effects of racial microaggressions on Black, Indigenous, and People of Color (BIPoC), primarily in the United States and Canada.We conclude by discussing the theory's application in Germany.
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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 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".