How racial microaggressions impact the campus experience of students of color
Bibliographic record
Abstract
Racism can take many forms, including explicit racism as well as subtle or covert racism, such as microaggressions. Research has shown that long-term and consistent exposure to racial microaggressions can lead to detrimental health outcomes such as stress, anxiety, depression, PTSD, and negative physical health outcomes, particularly among Black, Indigenous, and other People of Color (BIPOC). This study explores how racial microaggressions impact the psychological well-being and sense of belonging of BIPOC students at a large Canadian urban university. Additionally, it examines whether different BIPOC groups experience differential effects of racial microaggressions. A total of 403 self-identified BIPOC students were recruited through the School of Psychology subject pool, university-wide emails, social media, and campus organizations. A cross-sectional survey design was employed, with participants completing an online survey that included both investigator-developed questions about the campus climate and the following measures: the Racial Microaggressions Scale (RMAS), Racial Microaggressions in Higher Education Scale (RMHES), and Perceived Cohesion Scale (PCS). Statistical analyses assessed correlations between racial microaggressions, psychological distress, and belonging, with comparative analyses examining group differences. The findings revealed significant associations between racial microaggressions and negative emotional states. Black students reported the highest levels of racial microaggressions and trauma symptoms, highlighting their disproportionate burden. The results suggest that racial microaggressions contribute to heightened distress and reduced belonging among BIPOC students. These findings align with previous literature highlighting the harmful effects of subtle racism in academic settings. Addressing racial microaggressions is essential for improving inclusive and supportive academic environments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".