After Pierce and Sue: A Revised Racial Microaggressions Taxonomy
Bibliographic record
Abstract
Harvard psychiatrist Chester Pierce’s conception of “subtle and stunning” daily racial offenses, or microaggressions, remains salient even 50 years after it was introduced. Microaggressions were defined further by Sue and colleagues in 2007, and this construct has found growing utility as the deleterious effects of microaggressions on the health of people of color continues to mount. Many studies seek to frame microaggressions in terms of a taxonomic analysis of offender behavior to inform the assessment of and interventions for the reduction of racial microaggressions. This article proposes an expansion and refinement of Sue et al.’s taxonomy to better inform such efforts. We conducted a review of published articles that focused on qualitative and quantitative findings of microaggressions taxonomies ( N = 32). Sixteen categories of racial microaggressions were identified, largely consistent with the original taxonomy of Sue et al. but expanded in several notable ways. Building on our prior research, other researchers supported such new categories as tokenism, connecting via stereotypes, exoticization and eroticization, and avoidance and distancing. The least studied categories included the denial of individual racism from Sue et al., and newer categories included reverse-racism hostility, connecting via stereotypes, and environmental attacks. A unified language of microaggressions may improve understanding and measurement of this important construct.
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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 | 0.018 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.008 | 0.019 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".