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Record W2092569563 · doi:10.1086/671712

Paying the Piper: The High Cost of Funerals in South Africa

2008· article· en· W2092569563 on OpenAlexaboutno aff
Anne Case, Anupam Garrib, Alicia Menendez, Analía Olgiati

Bibliographic record

VenueEconomic Development and Cultural Change · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicInsurance, Mortality, Demography, Risk Management
Canadian institutionsnot available
FundersNational Institute on AgingWellcome TrustEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentPrinceton University
KeywordsQuarter (Canadian coin)Demographic economicsPer capitaEconomicsSocioeconomicsDemographyGeographyLabour economicsPopulationSociology

Abstract

fetched live from OpenAlex

We analyze funeral arrangements following the deaths of 3,751 people who died between January 2003 and December 2005 in the Africa Centre Demographic Surveillance Area. We find that, on average, households spend the equivalent of a year's income for an adult's funeral, measured at median per capita African (Black) income. Approximately one-quarter of all individuals had some form of insurance, which helped surviving household members defray some fraction of funeral expenses. However, an equal fraction of households borrowed money to pay for the funeral. We develop a model, consistent with ethnographic work in this area, in which households respond to social pressure to bury their dead in a style consistent with the observed social status of the household and that of the deceased. Households that cannot afford a funeral commensurate with social expectations must borrow money to pay for the funeral. The model leads to empirical tests, and we find results consistent with our model of household decision-making.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.101
GPT teacher head0.274
Teacher spread0.173 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations24
Published2008
Admission routes1
Has abstractyes

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