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Record W4220797410 · doi:10.1007/s11013-022-09772-7

‘White Child Gone Bankrupt’—The Intersection of Race and Poverty in Youth Fathered by UN Peacekeepers

2022· article· en· W4220797410 on OpenAlexafffund
Susan A. Bartels, Sanne Weber, Sabine Lee

Bibliographic record

VenueCulture Medicine and Psychiatry · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Security, and Conflict
Canadian institutionsQueen's University
FundersSocial Sciences and Humanities Research CouncilArts and Humanities Research Council
KeywordsPovertyPeacekeepingPrivilege (computing)DisadvantagedWhite privilegePolitical scienceDisadvantageRace (biology)Nexus (standard)Gender studiesSociologyPsychologyLaw

Abstract

fetched live from OpenAlex

Children fathered and abandoned by United Nations peacekeepers are an unintended consequence of peacekeeping operations. Research suggests that the social identity of peacekeeper-fathered children (PKFC) is complex and contradictory. While economically disadvantaged, PKFC's biracial background confers elements of racial privilege. Using the Democratic Republic of Congo as a case study, the present research evaluates the impact of racial differences on PKFC's social standing. Drawing on in-depth interviews with a racially heterogeneous sample of 35 PKFC and 60 mothers, we analyse how race and poverty interact and cause PKFC's conflicting social role. The data demonstrates that being of mixed race leads to the expectation of a higher living standard. Since most PKFC live in extreme economic deprivation, their anticipated privilege contrasts with reality. We found that the stigmatizing effects of poverty were amplified by biracial identification, leading to additional disadvantage, epitomised in the term "Muzungu aliye homba" [white child gone bankrupt]. The findings add to research on 'children born of war' and show the role of culture in shaping youth's social identities. Based on PKFC's intersecting burdens, we make policy recommendations that address the nexus of race and poverty.

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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.145
Threshold uncertainty score0.368

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 studies0.0000.000
Scholarly communication0.0000.000
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 head0.013
GPT teacher head0.262
Teacher spread0.248 · 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

Citations7
Published2022
Admission routes2
Has abstractyes

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