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Record W2299911250 · doi:10.1111/sjtg.12136

Exploring food choices within the context of nutritional security in Gaborone, Botswana

2016· article· en· W2299911250 on OpenAlexafffund
Alexander Legwegoh, Alice J. Hovorka

Bibliographic record

VenueSingapore Journal of Tropical Geography · 2016
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsQueen's UniversityUniversity of Guelph
FundersInternational Development Research Centre
KeywordsFood securityContext (archaeology)MalnutritionConsumption (sociology)Food choiceFood consumptionEnvironmental healthGeographyBusinessMarketingEconomic growthSociologyMedicineEconomicsAgricultural economicsAgriculture

Abstract

fetched live from OpenAlex

Food consumption experiences remain largely unexplored in urban Africa, despite mounting concerns regarding both over‐ and undernutrition of city dwellers and the potential impact on overall human health and development. This paper seeks to explore the foodstuffs people consume and the factors that shape consumer choice in Gaborone, Botswana. Empirical data were drawn from food diaries and observations of 40 households and discussions with them, plus key informants interviews in Gaborone. Analysis reveals the range of foodstuff people consume, highlighting the prevalence of diets comprised of energy dense, processed and animal‐sourced foods, which are major nutritional security concerns. However these diets were not summarily western or westernizing as per the nutritional transition thesis, as our analysis suggests subtleties of dietary patterns, including the fact that meat‐based diets are traditionally rooted rather than imported and meals tend to comprise both local and western components. The paper also identifies multiple interacting factors influencing consumer food choices, illustrating how food decisions embody context‐specific personal and social circumstances. Understanding how these factors shape what people eat in Gaborone may enable policy makers to facilitate the conditions within which healthy food choices can be made and to address emerging public health and nutrition challenges in African cities.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.355

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 communication0.0000.001
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 head0.035
GPT teacher head0.258
Teacher spread0.223 · 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

Citations14
Published2016
Admission routes2
Has abstractyes

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