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Record W2249467229

Gendered Mobilities, Food Access, and Rural-Urban Linkages in Blantyre, Malawi

2013· article· en· W2249467229 on OpenAlexaff
Liam Riley

Bibliographic record

VenueSSRN Electronic Journal · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicUrban Agriculture and Sustainability
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsLivelihoodFood securityMobilitiesGeographyEconomic growthRural areaPhysical accessFocus groupBusinessEconomic geographyPolitical scienceEconomicsSociologyAgricultureMarketing
DOInot available

Abstract

fetched live from OpenAlex

People in African cities access food from a variety of sources, including but not limited to: supermarkets; traditional marketplaces; informal vendors; urban and rural farms, and rural markets. Optimising the quality, price, and variety of foods requires mobility, which is often constrained by gender roles. This paper draws on participative mapping and in-depth interviews conducted in Blantyre, Malawi, a city in which robust urban-rural linkages have economic, political, and social dimensions that fundamentally shape urban household food security. The ability to frequently visit rural and peri-urban places to grow and buy food can mitigate the effect of inadequate and precarious urban incomes on household food security. The paper argues that gendered mobility constraints can impact the food security of entire households by hindering food access. The focus on urban residents moving between rural and urban spaces for their livelihoods highlights the centrality of urban-rural linkages for urban livelihoods. While existing urban food security research has focused on urban-rural linkages in terms of human migration and food supply chains, the gendered focus of this case study shows a consistent and vital stream of people and food crossing the urban/rural boundary and calls for new perspectives on spatial categories of analysis.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.139

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.004
Scholarly communication0.0020.002
Open science0.0000.003
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 head0.010
GPT teacher head0.202
Teacher spread0.192 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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