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Record W2127340200 · doi:10.5430/ijba.v6n2p146

The Impact of the Unofficial Cattle Business on the Household Welfare of Cattle Traders of the Border Towns of Cameroon and Nigeria

2015· article· en· W2127340200 on OpenAlexvenueno aff
Saidou Baba Oumar, Salihu Zummo Hayatudeen

Bibliographic record

VenueInternational Journal of Business Administration · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Rural Development Research
Canadian institutionsnot available
Fundersnot available
KeywordsPovertyWelfareGovernment (linguistics)PensionBusinessEconomic growthKenyaPoverty reductionEconomicsAgriculturePolitical scienceGeographyMarket economy

Abstract

fetched live from OpenAlex

This paper examines the impact of the unofficial cattle business on the household welfare of cattle traders of the border towns of Cameroon and Nigeria and relates that impact to the household access to basic needs or services of life such as income, employment, food, shelter, education, potable water, electricity, and health care that have been extensively used in the literature as indicators for the attainment of well-being and freedom from the yoke of poverty in the society. It uses primary and secondary data on the trade activities and employs descriptive as well as inferential techniques of data analysis to capture the objectives of the inquiry. The findings of the paper show that despite the unofficial character and unnoticed impact of the business by the governments of Cameroon and Nigeria, the traders who partake in the business acknowledge to have enhanced their living conditions with it. Poverty reduction being implicitly or explicitly cited as a strategy for household welfare improvement by the government in Cameroon and Nigeria, the paper recommends the enforcement, review and continuation of the existing poverty reduction programs irrespective of the leadership choice in the countries.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.690
Threshold uncertainty score0.100

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.036
GPT teacher head0.285
Teacher spread0.248 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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