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Record W2952121429 · doi:10.29333/ejecs/228

“I had Missionary Grandparents for Christ’s Sakes!”: White Women in Transracial/Cultural Families Bearing Witness to Whiteness

2019· article· en· W2952121429 on OpenAlexaffabout
Willow Samara Allen

Bibliographic record

VenueJournal of Ethnic and Cultural Studies · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicCritical Race Theory in Education
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsWitnessWhite (mutation)Gender studiesSociologyWhite supremacyPoliticsContext (archaeology)ColonialismWomen of colorRacismHistoryPolitical scienceRace (biology)Law

Abstract

fetched live from OpenAlex

White women have occupied a distinct position in histories of White supremacy. With the rise of White supremacist discourses in this current epoch, I posit now is a critical time to examine how White women can bear witness to their Whiteness and to ask what role they want to play in creating a more equitable future. I take up these considerations by drawing on interview data from a qualitative study of ten White women in transracial/cultural families with Black African partners to analyze how the participants conceptualize their Whiteness and how they can make connections between their subjectivities and histories of colonialism. The women’s articulations reveal that through new relational and spatial experiences across multiple forms of difference, White women can develop a changing relationship to Whiteness and what it represents in neocolonial spaces on the African continent, the Canadian settler colonial context, and within their own familial histories and relationships. Findings suggest that for White women to witness the historical weight of their Whiteness, forming linkages between their lives and broader political, economic, and social conditions of inequity is necessary. I argue White women need to create spaces of critical engagement, such as the spaces created in the study, where they can begin to imagine themselves as different racialized subjects.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.073
Threshold uncertainty score0.365

Codex and Gemma teacher scores by category

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

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 head0.069
GPT teacher head0.431
Teacher spread0.362 · 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

Citations3
Published2019
Admission routes2
Has abstractyes

Explore more

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