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Record W3009221936 · doi:10.24908/jcri.v7i1.12798

"Everyday Racism," "White Innocence," and Postcolonial Society: A Deeper Look into the Dutch Cultural Archive

2020· article· en· W3009221936 on OpenAlexvenueno aff
Bas Dikmans

Bibliographic record

VenueJournal of Critical Race Inquiry · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicCultural Heritage Management and Preservation
Canadian institutionsnot available
Fundersnot available
KeywordsRealmRacismInnocenceGender studiesSociologyTurkishWhite (mutation)White privilegeHegemonyColonialismImmigrationAestheticsPoliticsMedia studiesLawPolitical scienceArt

Abstract

fetched live from OpenAlex

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. 

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.601
Threshold uncertainty score0.697

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.095
GPT teacher head0.309
Teacher spread0.214 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2020
Admission routes1
Has abstractyes

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