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Record W4391103192 · doi:10.1057/s41599-024-02620-6

Comparative analysis of youth transition in bean production systems in Ghana and Cameroon

2024· article· en· W4391103192 on OpenAlexfundno aff
Eileen Bogweh Nchanji, Patricia Pinamang Acheampong, Siri Bella Ngoh, Victor Nyamolo, Lutomia Cosmas

Bibliographic record

VenueHumanities and Social Sciences Communications · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Innovations and Practices
Canadian institutionsnot available
FundersGlobal Affairs CanadaBill and Melinda Gates Foundation
KeywordsFocus groupPovertyProduction (economics)AgriculturePhaseolusGeographyValue chainSocioeconomicsAgricultural productivityPolitical scienceBusinessEconomic growthAgricultural scienceBiologyEconomicsAgronomy

Abstract

fetched live from OpenAlex

Abstract Youth transition in the common bean ( Phaseolus vulgaris L.) value chain remains low in Ghana and Cameroon despite the potential of the bean sub-sector in reducing poverty, unemployment, and undernutrition. This study compared youth transition in the bean value chain in Ghana and Cameroon. It investigated how intersectional elements, including age, influence the uptake of bean production among the youth in these two countries. Data were collected from 266 participants from Ghana and 84 from Cameroon. The data were collected through focus group discussions (FGD) and in-depth interviews. The results demonstrated that Ghanaian youth disfavored bean production, while in Cameroon, youth favored bean production. In both cases, parents were instrumental in influencing youth choices. In Ghana, many parents did not approve of their children taking bean production as a primary occupation. By contrast, parents in Cameroon favored bean production and appeared to value agriculture, thus encouraging their children to venture into it. Despite the differences in Ghanaian and Cameroonian youth’ perceptions of agriculture, the challenges they faced that hindered their participation in the bean value chain remained the same: lack of financial support, limited access to land, and lack of technical know-how.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.497
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.232
GPT teacher head0.334
Teacher spread0.102 · 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 designQualitative
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

Citations5
Published2024
Admission routes1
Has abstractyes

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