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Record W1499264192 · doi:10.4000/vertigo.15980

Diversité alimentaire et prééminence du riz dans les habitudes de consommation au Cameroun

2015· article· fr· W1499264192 on OpenAlexvenueno aff
Sariette Batibonak, Claudine Defo

Bibliographic record

VenueVertigO · 2015
Typearticle
Languagefr
FieldAgricultural and Biological Sciences
TopicAgriculture and Rural Development Research
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

Le Cameroun dispose d’un potentiel naturel sous-exploité. Des dizaines d’aliments d’origine végétale permettent d’apprécier un pan de l’importance de ses ressources. La richesse de ses forêts et de ses terres est attestée par diverses études. Malgré cette multitude de ressources alimentaires, seule une quinzaine d’entre elles font partie des choix alimentaires des populations. Cependant, depuis des décennies, force est de constater que les populations recourent prioritairement au riz, négligeant ainsi les richesses alimentaires à disposition. Comment comprendre cet enclin à consommer principalement les produits rizicoles ? À travers cet article de réflexion, nous esquissons un paradoxe entre ce potentiel en ressources alimentaires et le choix des populations portées vers la consommation du riz dans un contexte de mutation des habitudes alimentaires. Par une approche anthropologique, la réflexion aboutit à la conclusion selon laquelle les ménages camerounais portent leur dévolu prioritairement sur les produits rizicoles.

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)0.002
metaresearch head score (Gemma)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.033
GPT teacher head0.254
Teacher spread0.222 · 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
Published2015
Admission routes1
Has abstractyes

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Same venueVertigOSame topicAgriculture and Rural Development ResearchFrench-language works237,207