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Record W2171259793 · doi:10.5539/sar.v3n4p9

Assessing the Financial Health Status of Small Scale Poultry Businesses in Delta State, Nigeria

2014· article· en· W2171259793 on OpenAlexvenueno aff
D. E. Idoge, Christopher O. Chukwuji

Bibliographic record

VenueSustainable Agriculture Research · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicWorking Capital and Financial Performance
Canadian institutionsnot available
Fundersnot available
KeywordsSolvencyAgribusinessAgricultural scienceBusinessScale (ratio)Standard scoreDescriptive statisticsBankruptcyFinanceAgricultural economicsAgricultureStatisticsGeographyEconomicsMathematicsMarket liquidityEnvironmental science

Abstract

fetched live from OpenAlex

<p>The study investigated the financial health status of small scale poultry businesses in Delta State, Nigeria using Altman’s Z-score model. The empirical study was undertaken to assess the solvency and hence future survivability of small scale poultry enterprises in the State. Financial data were extracted from three years (2010 – 2012) financial statements of 125 small scale poultry farms purposively selected from farms operating in the State and incorporated with the Nigerian Corporate Affairs Commission as limited liability agribusinesses. Descriptive statistics which include computed financial ratios, frequency distributions, percentages and tables were applied to analyze the content of the financial statements and Altman’s Z-scores’ were computed for each sampled farm for the three year period. The study shows that in 2010, 47.8 percent of farm enterprises had Z-scores between minus 0.60 to 1.55. In 2011 and 2012, 44.8 percent and 42.4 percent, respectively of the farms had Z-scores between negative 0.60 and 1.55. The study further indicates that 28 percent, 27 percent and 30.4 percent in 2010, 2011 and 2012, respectively, of the sampled farms had computed Z-scores between 2.64 and 4.79 farms. The study recommends the use of Altman’s Z-score by small scale investors as a technique for monitoring the financial health of their agribusinesses to prevent the ugly consequences of bankruptcy and liquidation.</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)0.003
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 score0.164
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.023
GPT teacher head0.295
Teacher spread0.272 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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