A qualitative study of microaggressions against African Americans on predominantly White campuses
Bibliographic record
Abstract
BACKGROUND: Pierce's (The Black seventies: an extending horizon book, 1970) 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 (Am Psychol 62:271, 2007), and this construct has found growing utility as the deleterious effects of microaggressions on the health of people of color continues to mount. Microaggressions are common on campuses and contribute to negative social, academic, and mental health outcomes. METHOD: This paper explores how Black college students' experiences correspond to or differ from the microaggression types originally proposed by Sue et al. (Am Psychol 62:271, 2007). Themes were identified from focus group data of students of color (N = 36) from predominately White institutions (PWIs) of higher learning (N = 3) using interpretative phenomenological analysis. RESULTS: We identified 15 categories of racial microaggressions, largely consistent with the original taxonomy of Sue et al. but expanded in several notable ways. New categories in our data and observed by other researchers, included categories termed Connecting via Stereotypes, Exoticization and Eroticization, and Avoidance and Distancing. Lesser studied categories identified included Sue et al.'s Denial of Individual Racism, and new categories termed Reverse Racism Hostility, Connecting via Stereotypes, and Environmental Attacks. DISCUSSION: While previous literature has either embraced the taxonomy developed by Sue and colleagues or proposed a novel taxonomy, this study synthesized the Sue framework in concert with our own focus group findings and the contributions of other researchers. Improving our understanding of microaggressions as they impact people of color may better allow for improved 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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.013 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".