Visceral Geographies of Whiteness and Invisible Microaggressions
Bibliographic record
Abstract
Drawing on data from focus groups, we demonstrate and analyze how racial microaggressions impact people of color, in unique and often traumatizing ways. We do so by including the eye opening stories of graduate students and faculty of color, taking seriously the call of critical race theorists to incorporate storytelling into scholarship. We argue that the experiences people of color undergo provide a unique perspective on visceral geographies in part because their voices are silenced; reacting internally is often the only safe response in an overwhelmingly white discipline. By starting at the scale of the body, we combine theories on visceral geographies with theories of racial microaggressions to reveal how whiteness permeates geography at multiple scales and spaces. We also examine the visceral within intellectual spaces of geography as a discipline and geography departments. We further explain how intersections of race, gender, and sexuality influence the visceral reactions of people of color to microaggressions in geography departments. Our findings demonstrate how racist behaviors take up space in departments, in the process of intellectual production and in the bodies of non-white geographers.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".