Higher Racism: The Case of the University of British Columbia— On the Wrong Side of History but Right Side of Optics
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
Higher education “elites often see themselves as moral leaders and will therefore generally dissociate themselves from anything that has to do with racism as they define it.” In this era of Black Lives Matter (BLM), this narrative is especially comforting for the managers as they invest in optics to sustain rankings and revenue. This article specifically draws on the case of the University of British Columbia (UBC) to address the racial demographics of hiring and appointing African ethnic and diasporic faculty and administrators. We provide various examples of how the institution functions through racial bias and prejudice but argue that leaving the explanation to structural or systemic racism makes it too easy to deny elite individual and everyday racism, especially racist attitudes and decisions of the managers and their means of employment discrimination. Insider knowledge of employees is crucial as managers respond to racism within their workplaces. In this case, as UBC managers try to get out in front of BLM and control the optics, it’s important to provide a critical analysis of reality and recent history of administrative inaction. Finally, we articulate concerns that managers are preferring to isolate and shield themselves from critical conversation and critique of mismanagement.
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 | 0.000 | 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 | 0.000 | 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) | 0.001 | 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