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Record W1981910287 · doi:10.4054/demres.2011.24.27

HIV/AIDS and time allocation in rural Malawi

2011· article· en· W1981910287 on OpenAlexaff
Ari Van Assche, Phil Anglewicz, Peter Fleming, Catherine van de Ruit

Bibliographic record

VenueDemographic Research · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicPoverty, Education, and Child Welfare
Canadian institutionsHEC MontréalUniversité de Montréal
FundersEunice Kennedy Shriver National Institute of Child Health and Human Development
KeywordsHuman immunodeficiency virus (HIV)Rural areaGeographySocioeconomicsEconomic growthDemographyEconomicsMedicineSociologyVirology

Abstract

fetched live from OpenAlex

AIDS-related morbidity and mortality are expected to have a large economic impact in rural Malawi, because they reduce the time that adults can spend on production for subsistence and on income-generating activities. However, households may compensate for production losses by reallocating tasks among household members. The data demands for measuring these effects are high, limiting the amount of empirical evidence. In this paper, we utilize a unique combination of qualitative and quantitative data, including biomarkers for HIV, collected by the 2004 Malawi Diffusion and Ideational Change Project, to analyze the association between AIDS-related morbidity and mortality, and time allocation decisions in rural Malawian households. We find that AIDS-related morbidity and mortality have important economic effects on women's time, whereas men's time is unresponsive to the same shocks. Most notably, AIDS is shown to induce diversification of income sources, with women (but not men) reallocating their time, generally from work-intensive (typically farming and heavy chores) to cash-generating tasks (such as casual labor).

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)0.000
metaresearch head score (Gemma)0.001
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 score0.030
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.051
GPT teacher head0.345
Teacher spread0.294 · 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

Citations13
Published2011
Admission routes1
Has abstractyes

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