MétaCan
Menu
Back to cohort
Record W4403400661 · doi:10.3390/admsci14100256

Agri-Preneurial Resilience and Success: The Correlation and Demographic Characteristics of Smallholders in South Africa

2024· article· en· W4403400661 on OpenAlexaff
Isaac Azikiwe Agholor, Ataharul Chowdhury, Shehu Folaranmi Gbolahan Yusuf

Bibliographic record

VenueAdministrative Sciences · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Socioeconomic Development
Canadian institutionsUniversity of Guelph
FundersUniversity of Mpumalanga
KeywordsResilience (materials science)GeographyEconomic geographySocioeconomicsAgricultural economicsEconomics

Abstract

fetched live from OpenAlex

The incentives and subsidies needed to stimulate growth, resilience, and success in agri-preneurial businesses will only be realized through numerous interventions as agri-preneurship contributes significantly to sustainable agricultural development in South Africa. This study provided a novel insight into agri-preneurial resilience and success and evidenced the hypothesis that there is no significant positive correlation between agri-preneurial resilience, farm success, and selected demographic characteristics of smallholders. We surveyed a total of 200 agri-preneurs who were willing and able to participate in this study. This study used a structured questionnaire that was divided into the following sections: (i) demographic information; (ii) agri-preneurial resilience; and (iii) agri-preneurial success. Descriptive statistics and a regression analysis were employed to articulate responses. Four distinct models were employed to ascertain the goodness of fitness and the hypothesis, and assess the relationship between success, resilience, and selected demographic characteristics of agri-preneurs. To determine resilience, the Connor–Davidson Resilience Scale (CD-RISC) with 10 items was used because the CD-RISC justifies the best psychometric characteristics that portrays the levels of resilience amongst agri-preneurs. In measuring success, the scale items were graduated and ranked on a 5-point scale from 1 to 5. The reliability of the scale was also tested, and α = 0.93 was obtained. This study obtained a Cronbach alpha value of 0.96, indicating optimum reliability. Additionally, we ran a factor analysis to certify the reliability of the variable, which gave one factor from the four items. Significant positive correlations were found between gender, age, education, income, household size, diversification, and agri-preneurial resilience and success. This study concluded that most of the selected demographic characteristics were predictors of agri-preneurial resilience and success. However, demographic variables may be influenced by numerous factors given the heterogeneity of agri-preneurs in the study area.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.294
Threshold uncertainty score0.276

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 studies0.0000.001
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 head0.038
GPT teacher head0.263
Teacher spread0.226 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueAdministrative SciencesSame topicInnovation and Socioeconomic DevelopmentFrench-language works237,207