MétaCan
Menu
Back to cohort
Record W2558397512 · doi:10.5539/jsd.v9n6p96

Nyama Choma Culture: Implications of Increased Red Meat and Alcohol Consumption in East Africa

2016· article· en· W2558397512 on OpenAlexvenueno aff
Irena Gorski, Wan-Chen Chung, Kelli Herr, Khanjan Mehta

Bibliographic record

VenueJournal of Sustainable Development · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Rural Development Research
Canadian institutionsnot available
FundersWorld Cancer Research Fund InternationalU.S. Department of Agriculture
KeywordsChampionSustainabilityConsumption (sociology)Red meatValue (mathematics)OverconsumptionFood systemsVariety (cybernetics)Economic growthPolitical scienceDevelopment economicsEconomicsGeographySociologyProduction (economics)BiologySocial scienceEcologyFood scienceFood security

Abstract

fetched live from OpenAlex

Nyama Choma Culture (NCC) reflects a way of life in East Africa based on the increased consumption of red meat and alcohol which seemingly projects a higher social status. The rising emergence of these cultural practices has positive as well as negative implications on individuals, their families, and larger socio-economic systems. This article draws from a variety of sources to provide an objective description of the implications of NCC on the people adopting it as well as society at large. These concerns are categorized into four primary areas: public health, the food industry, climate change, and community microeconomics. As the dynamics and impacts of NCC are already being felt in developing countries, this cultural phenomenon calls for careful monitoring and consideration in the design of policies and practices to champion the economic, social, and environmental sustainability of community nutrition systems and regional food value chains.

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)0.000
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 score0.053
Threshold uncertainty score0.105

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
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 head0.026
GPT teacher head0.243
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 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

Citations10
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Sustainable DevelopmentSame topicAgriculture and Rural Development ResearchFrench-language works237,207