MétaCan
Menu
Back to cohort
Record W3164333425 · doi:10.1080/0376835x.2021.1932423

Household food security in Maputo: the role of Gendered Access to education and employment

2021· article· en· W3164333425 on OpenAlexafffund
Cameron McCordic, Liam Riley, Inês Raimundo

Bibliographic record

VenueDevelopment Southern Africa · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicUrban Agriculture and Sustainability
Canadian institutionsWilfrid Laurier UniversityBalsillie School of International AffairsUniversity of Waterloo
FundersSocial Sciences and Humanities Research Council of CanadaInternational Development Research Centre
KeywordsFood securityPovertyFood insecurityPoliticsEconomic growthInequalityPolitical scienceDevelopment economicsSocioeconomicsEconomicsSociologyGeographyAgriculture

Abstract

fetched live from OpenAlex

Gender-based structural inequalities in Southern African cities continue to drive poverty and food insecurity in spite of decades of development efforts to raise the social, economic, and political status of women relative to men. A 2014 survey of household food security in Maputo found that female headship is closely associated with food insecurity. This article assesses the role of employment and education in explaining this phenomenon in the city of Maputo. Using household survey data, this investigation defines the extent to which the relationship between the sex of the household head and food insecurity appears to be conditionally dependent upon employment and education. The findings provide further impetus to urban policy makers to operationalise gender equality goals.

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)0.002
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 score0.036
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.027
GPT teacher head0.214
Teacher spread0.187 · 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

Citations12
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueDevelopment Southern AfricaSame topicUrban Agriculture and SustainabilityFrench-language works237,207