Examining environmental racial microaggressions on a university campus
Bibliographic record
Abstract
College campuses are becoming increasingly diverse; yet there are many ways in which the university climate fails to promote inclusion, thereby creating a sense of exclusion for students of colour. This study utilizes a visual content analysis to critically examine the spatial imagery of exclusionary messages on a predominantly White institution (PWI) campus. Specifically, we asked student collaborators (N = 3) to identify and reflect upon cultural artefacts and imagery within their university that conveyed messages of inclusion or exclusion, affecting their sense of belonging in the campus environment. The students captured eight photographs and provided written narratives to further contextualize their observations. Four main themes emerged from the analysis to represent the messages conveyed to people of colour: (1) tokenism and visual differentiation; (2) selective visibility and stereotyping; (3) lack of belonging and empowerment; and (4) white saviorism. Our findings revealed the pervasive environmental microaggressions that students observed in images, portraits, and illustrations across campus at this PWI. These images and their underlying messages can encourage higher education institutions to reflect deeply on their practices in order to truly foster a culture of belonging on campus. By doing so, they might move beyond superficial impression management to undertake more substantial evaluations and improvements in their current practices. Finally, we present practical recommendations to promote genuine diversity and inclusion on university campuses.
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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 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".