School Without Racism? How White Teachers in Germany Practice Anti-Racialism
Bibliographic record
Abstract
This qualitative study investigates how white teachers at a German Catholic comprehensive school conceptualize issues of “race” and racism in the context of being a “School without Racism – School with Courage” (SOR-SMC). By collecting signatures and exhibiting yearly projects, more than 3,300 schools in Germany brand their school to be “without racism”. I found the branding of my researched school to be a form of “anti-racialism” that opposed “race” and racism as concepts but did not tackle any underlying racist structures (Goldberg 2009, 10). The teachers I interviewed took the SOR-SMC branding for granted and assumed that the school was racism-free. They thereby engaged in silent racism and reproduced racist connotations and structures without challenging them (Trepagnier 2001). Being anti -racist is not accomplished by declaring a school as racism-free. Instead, white teachers need to understand that anti-racism involves a deeper engagement with the structures that keep “racial” inequality in place (Goldberg 2009, 10).
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".