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Record W2064240149 · doi:10.14506/ca29.4.03

“Too Fat to Be an Orphan”: The Moral Semiotics of Food Aid in Botswana

2014· article· en· W2064240149 on OpenAlexaff
Bianca Dahl

Bibliographic record

VenueCultural Anthropology · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicPoverty, Education, and Child Welfare
Canadian institutionsUniversity of Toronto
FundersWenner-Gren FoundationU.S. Department of EducationBrown UniversityNational Science Foundation
KeywordsKinshipSemioticsClothingGlocalizationPoliticsSociologyGender studiesPolitical scienceMedia studiesAnthropologyLawGlobalization

Abstract

fetched live from OpenAlex

The iconography of the African AIDS orphan, captured in National Geographic–style images of half-starved toddlers with distended bellies, inspires humanitarian aid for the continent. In Botswana, stereotypes underlying both foreign-funded and governmental programs for orphaned children—which imply that orphans are underfed and underloved—initially resonated with Tswana people’s anxieties that neglect by overburdened kin results in parentless children going hungry. However, during the past decade international feeding projects began to evolve into elaborate day-care complexes in which village orphans gained exclusive access to swimming pools, DVDs, trendy clothing, and daily meat rations. This article traces the shifting moral semiotics of orphans’ fat and skinny bodies, explaining why new discourses protesting the overfattening of orphans arose in a southeastern village. Metaphors of fat and feeding have become a scale on which the excesses of humanitarian aid and the perceived shortcomings of local kinship practices are weighed. A new kind of “politics of the belly” calls into question relations of patronage around metaphors of fleshiness and dependence on foreign support. In the process, contestations over children’s skinny and fat bodies lead to reconfigurations of the idea of orphanhood.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.674
Threshold uncertainty score0.997

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 studies0.0010.001
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 head0.042
GPT teacher head0.342
Teacher spread0.300 · 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 designNot applicable
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

Citations29
Published2014
Admission routes1
Has abstractyes

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