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Record W2035833036 · doi:10.3390/su5114728

Wild Food, Prices, Diets and Development: Sustainability and Food Security in Urban Cameroon

2013· article· en· W2035833036 on OpenAlexaff
Lauren Q. Sneyd

Bibliographic record

VenueSustainability · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicUrban Agriculture and Sustainability
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsFood securitySustainabilityVulnerability (computing)BusinessPopulationStaple foodConsumption (sociology)Resource (disambiguation)Agricultural economicsGeographyEconomicsEnvironmental healthAgricultureBiologyEcology

Abstract

fetched live from OpenAlex

This article analyses wild food consumption in urban areas of Cameroon. Building upon findings from Cameroon’s Comprehensive Food Security and Vulnerability Analysis (CFSVA) this case study presents empirical data collected from 371 household and market surveys in Cameroonian cities. It employs the UN Special Rapporteur on the Right to Food’s framework for understanding challenges related to the availability, accessibility, and adequacy of food. The survey data suggest that many wild/traditional foods are physically available in Cameroonian cities most of the time, including fruits, vegetables, spices, and insects. Cameroonians spend considerable sums of their food budget on wild foods. However, low wages and the high cost of city living constrain the social and economic access most people have to these foods. The data also suggest that imports of non-traditional staple foods, such as low cost rice, have increasingly priced potentially more nutritious or safe traditional local foods out of markets after the 2008 food price crisis. As a result, diets are changing in Cameroon as the resource-constrained population continues to resort to the coping strategy of eating cheaper imported foods such as refined rice or to eating less frequently. Cameroon’s nutrition transition continues to be driven by need and not necessarily by the preferences of Cameroonian consumers. The implications of this reality for sustainability are troubling.

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 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.054
Threshold uncertainty score0.851

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.007
GPT teacher head0.192
Teacher spread0.185 · 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 teacher head, 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

Citations61
Published2013
Admission routes1
Has abstractyes

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