Visceral Geographies of Whiteness and Invisible Microaggressions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it