white Feelings and Black Knowledge: Tackling Racism and Epistemic Violence in German Higher Education
Bibliographic record
Abstract
This article provides insight on the production and passing on of “knowledge” and the idea of a possible objectivity particularly conveyed in Western universities. It deals with how forming a university group tackles the continuations of such thoughts. The article starts by reflecting on statements and practices of a university seminar of which I was a participant. The course examined hip hop culture through language, investigating lyrics from a cultural linguistics perspective. It served as an example of epistemic violence and racist continuations and reproductions at universities that try to promote their anti “whatever –isms” attitudes. Next, I provide theoretical background on the fantasy of academic objectivity and its resultant racism, and explain the formation of a Black university group. I will explore the group’s experiences and struggles to critique Western universities and knowledge transfer while actively being part of them. The group’s work is an exemplary way of how to question existing structures and of resisting imposed and racist ideas of knowledge and truths.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.021 | 0.043 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".