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Record W3109826946 · doi:10.1177/0042098020963849

Single mothers coping with food insecurity in a Nairobi slum

2020· article· en· W3109826946 on OpenAlex
Sangeetha Madhavan, Shelley Clark, Sara Schmidt

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueUrban Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicUrban and Rural Development Challenges
Canadian institutionsMcGill University
FundersEunice Kennedy Shriver National Institute of Child Health and Human Development
KeywordsSlumFood securityVulnerability (computing)PovertyFood insecurityUrbanizationEconomic growthInequalityDevelopment economicsGeographySociologyEconomicsPopulation

Abstract

fetched live from OpenAlex

With high urbanisation rates, cities in sub-Saharan Africa are contending with food insecurity. Urban studies scholars have approached the issue mainly from the perspective of food deserts. We adapt Sen’s ‘resource bundles’ and Watts and Bohles’s ‘space of vulnerability’ concepts to examine food insecurity as a function of both tangible and intangible resources. Moreover, we also interrogate the role of kin in strengthening safety nets for the urban poor. Drawing on a data set of 462 single mothers in a slum in Nairobi, Kenya, we find that (1) bundles comes in four types; (2) bundles with high levels of all resources buffer against food insecurity as do (3) bundles weighted with high levels of wealth and social standing; and (4) kin enhance the protective effect of bundles only for two types. These findings should direct urban poverty researchers to consider the compounding effect of resources in the reproduction of poverty and social inequality and encourage policy makers to focus on both vulnerability and resilience in designing interventions to ensure food security.

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.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.680
Threshold uncertainty score0.343

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 studies0.0000.000
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 head0.111
GPT teacher head0.290
Teacher spread0.179 · 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