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Record W3127546682 · doi:10.5430/ijba.v12n1p57

The Proof of Economic Welfare Is in the Eating of Nutritious Diets by the Resource-Poor: A Political Economy Analysis of the International Evidence of Dietary Consumption in Rural Africa

2021· article· en· W3127546682 on OpenAlexvenueno aff
Gamel Abdul-Nasser Salifu

Bibliographic record

VenueInternational Journal of Business Administration · 2021
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsnot available
Fundersnot available
KeywordsWelfareMalnutritionDiversity (politics)Empirical evidenceDevelopment economicsEconomicsConsumption (sociology)PoliticsEconomic growthPublic economicsSociologyPolitical scienceSocial science

Abstract

fetched live from OpenAlex

This paper provides a summary of the international literature published in the thematic area of household dietary diversity with emphasis on the importance of sustainable approaches to global hunger and malnutrition. The paper highlights the correlation between inadequate household dietary diversity, child mortality and death. It also draws attention to the subtle combinations of social, economic and political interactions in enhancing nutritional welfare. The paper further raises a major argument that contemporary explorations of dietary diversity have failed to sustain the important tension and dialectical debate between diverse empirical realities and wider theorization of the concrete linkage of economic welfare to dietary diversity. The paper sets the tone for wider conceptualization of the phenomenon by clearly outlining the role of multiple relations of low incomes, inadequate maternal-child health care as well as poor environmental sanitation as leading predicators of under-nutrition in rural Africa. This theorization makes the case that, studying the relations and conjunctions of economic welfare via the agency of dietary consumption, is critical for emergent processes of change as the given-evidence of under-nutrition does not allow economists to draw solid conclusions on status of dietary diversity in rural Africa without a dynamic understanding and explicit recognition of the wider abstractions of political economy undercurrents in economic welfare and nutritional well-being literature. That said, the paper re-affirms the subtle evidence that under-nutrition and reduced body-mass of adults, often cited in the development literature as proxy-indicators of poor dietary diversity, needs to be re-evaluated as reduced body-mass in adults could be an indication of other incidences of environmental-stresses rather than of food-energy stress. In this vein, the review sustains a new argument that, resource-poor households modify attitude to food consumption in order to self-protect against income losses in household production systems. This position has implications for economic welfare and nutritional diversity research as food may not always come first for the resource-poor. It also opens new avenues for economic research by projecting the hypothesis that consumption of nutritious diets cannot be seen as a unique object to be sought by resource-poor to the exclusion of all other priorities including raising income frontiers through savings for wealth accumulation. The pre-eminence of food security over income diversification activities of rural households as a broader strategy to reduce malnutrition and poverty in the developing world could be re-examined to give livelihood activities a central role in household dietary diversity research.

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)0.004
metaresearch head score (Gemma)0.010
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0020.008
Scholarly communication0.0040.003
Open science0.0000.003
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 head0.032
GPT teacher head0.315
Teacher spread0.282 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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