MétaCan
Menu
Back to cohort
Record W4229443498 · doi:10.4314/ljh.v32i2.7

Food system flows and distribution for the Accra metropolis: Unfolding the policy dimensions

2022· article· en· W4229443498 on OpenAlexaff
Benjamin D. Ofori, Opoku Pabi, Daniel Nukpezah, John Jude Kweku Annan, Hsi‐Chuan Wang

Bibliographic record

VenueLegon Journal of the Humanities · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicUrban and Rural Development Challenges
Canadian institutionsUniversity of Toronto
FundersUniversity of Ghana
KeywordsFood distributionDistribution (mathematics)General partnershipBusinessFood policyCommodityFood systemsGovernment (linguistics)Economic growthCity regionProvisioningFood securityEconomicsGeographyEconomyAgriculturePolitical scienceFinance

Abstract

fetched live from OpenAlex

The continuous growth of cities in developing countries portends the challenge of food provisioning. This study therefore examined the policy dimensions of food flows and distribution for the Accra metropolis. The study methodology involved review of policy documents, interviews with government officials and city authorities, and discussions with a cross-section of food commodity traders at the city’s markets. The study established that the city’s food region is very extensive with the principal food staples originating from distant areas. The city’s local markets (and numerous informal markets) are very important in the food distribution network. However, they are characterised by inadequate infrastructure, poor waste management and congestion in view of poor planning. There is no composite national policy on city food supply and distribution apart from discrete programmes that seek to encourage partnership between city authorities and the private sector for the development of market infrastructure. The paper advocates for comprehensive national policy to ensure well-defined rural-urban linkages and city level agenda for sustainable food access in the city.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.946
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0070.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.058
GPT teacher head0.275
Teacher spread0.217 · 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.

Study designTheoretical or conceptual
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

Citations1
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueLegon Journal of the HumanitiesSame topicUrban and Rural Development ChallengesFrench-language works237,207