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Record W2092710452 · doi:10.5430/ijfr.v4n4p146

Financial Management in Local Government: The Nigeria Experience

2013· article· en· W2092710452 on OpenAlexvenueno aff
Nwosu M. Eze, Okafor O. Harrison

Bibliographic record

VenueInternational Journal of Financial Research · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsnot available
Fundersnot available
KeywordsTreasuryLocal governmentFinancePublic financeBusinessFinancial managementGovernment (linguistics)Statutory lawPopulationState (computer science)Central governmentEconomicsPublic administrationPolitical science

Abstract

fetched live from OpenAlex

As a creature of the constitution, it is the statutory responsibility of local government as the third tier of government to cater for the people at the grass root level which forms the preponderant majority of the teaming population of the whole country. It is a universally acknowledged that no meaningful economic planning and development can be made without finance. Determining the key roles of finance managers and processes of financial management is central to better fiscal outcomes. This paper examined financial management systems in the local governments in Nigeria. It primarily evaluated the roles, powers and challenges of the treasury department of local government in Nigeria. It revealed that lack of adequate technical capacity and constitutional loopholes associated with the creation of the state and local government joint accounts are the key factors affecting efficient financial management in the local government. The paper recommends that financial autonomy and improved technical capacity of treasury department of local government would promote sound financial management in the local government.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.036
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0080.003
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.059
GPT teacher head0.317
Teacher spread0.258 · 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 source (direct Gemma or distilled Codex), 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

Citations9
Published2013
Admission routes1
Has abstractyes

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