"Everyday Racism," "White Innocence," and Postcolonial Society: A Deeper Look into the Dutch Cultural Archive
Bibliographic record
Abstract
This paper explores Dutch postcolonial society through looking at it from the lens of critical race studies. In particular, this paper highlights the complex societal debate surrounding race, skin colour, and ethnicity in the Netherlands through examining the voices of Dutch individuals who come from an immigrant background and who are writing critically about these issues. Through looking at Surinamese-Dutch anthropologist Gloria Wekker’s “white innocence” and Surinamese-Dutch critical scholar Philomena Essed’s “everyday racism,” this paper explains how colonial discourses of racial thinking still substantially influence Dutch society today. I then employ these concepts to examine the nonfiction writings of the Russian-Cameroonian-Dutch author Anousha Nzume’s Hallo Witte Mensen[1](2017)and the Turkish-Dutch Zihni Özdil’s Nederland mijn Vaderland[2](2015) as two texts that critically engage in, within the realm of popular culture, contemporary discussions about the position of race and the way it is ingrained in the dominant conception of Dutch national identity. In doing so, I provide insight into how “new” migrant-descent voices within the realm of Dutch popular media are actively challenging hegemonic ideas about race and racism. [1]English translation: Hello White People. [2]English translation: The Netherlands, my Motherland.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.018 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".