“Dutch Racism is not Like Anywhere Else”: Refusing Color-Blind Myths in Black Feminist Otherwise Spaces
Bibliographic record
Abstract
Despite myths of color-blindness in the Netherlands, Black women are marginalized by mainstream expectations of racial and cultural homogeneity. I use Amsterdam Black Women as a case study to illustrate the lived experiences of women affected by this exclusion. In this space, women freely critique Dutch society through mundane moments of truth-telling, venting, and joking, which enable individual problems to rise to a community level. I explore how subtle configurations of Black feminist organizing can be key sites of healing, experimentation, and political engagement. This research complicates how we understand experiences of misogynoir when race consciousness is blended in a transnational context and how Amsterdam Black Women has made possible the refusal of Dutch norms that require members to accept their oppression silently and support false narratives of progressiveness and color-blindness.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.041 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| 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".