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Record W2124257680 · doi:10.5539/sar.v2n1p181

Analysis of Price Variation in the Marketing of Garri in Delta State, Nigeria

2012· article· en· W2124257680 on OpenAlexvenueno aff
Solomon Okeoghene Ebewore, Sunday Ifeanyi Ukwuaba, John Egbodion

Bibliographic record

VenueSustainable Agriculture Research · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Economics and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessMarketingDescriptive statisticsOrder (exchange)Sample (material)Government (linguistics)Local government areaAgricultural scienceAgricultural economicsEconomicsLocal governmentGeographyFinanceBiology

Abstract

fetched live from OpenAlex

<p>The study examined price variation in the marketing of garri in Delta State Nigeria. A multi-stage sampling procedure was used in drawing up a sample of 180 garri marketers from six purposively selected major garri markets each from six purposively selected Local government areas. Data collected with the aid of questionnaire were analysed using both descriptive and inferential statistics. The results revealed that majority of the marketers were females that are still in their economically active age group and relatively literate. Majority of the marketers were middlemen who sold mostly to their fellow middlemen. Most of the respondents agreed that marketing cost is the major cause of price variation in garri market while season of the year was the most notable problem facing garri marketers in the study area. Also, the result of Analysis of Variance revealed that there were significant differences in garri prices among the six markets. It was concluded that in order to stabilize price of garri and income of garri marketers, in order to ensure sustainable food security in Delta State, measures should be taken to provide adequate transportation and establish storage facilities. This will invariably cut down marketing costs.</p>

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)0.009
metaresearch head score (Gemma)0.001
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 score0.070
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.005
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.029
GPT teacher head0.302
Teacher spread0.273 · 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
Published2012
Admission routes1
Has abstractyes

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