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Record W2985624732 · doi:10.5539/ibr.v12n12p50

Evaluating Anti-Graft Agencies Governance Practices in Nigeria

2019· article· en· W2985624732 on OpenAlexvenueno aff
Uket Ewa, Adebisi Wasiu Adesola, Kechi Kankpang

Bibliographic record

VenueInternational Business Research · 2019
Typearticle
Languageen
FieldComputer Science
TopicCybercrime and Law Enforcement Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAccountabilityTransparency (behavior)Nonprobability samplingLanguage changeOpenness to experienceExtant taxonCorporate governanceGovernment (linguistics)State (computer science)ConstitutionPublic administrationAccountingBusinessLawPolitical scienceSociologyFinancePsychology

Abstract

fetched live from OpenAlex

The Nigerian state has witnessed exponential increase in corruption and various anti-graft agencies have been established by government to curb this malaise which has branded the Nigerian state and its citizens all over the world as corrupt. The agencies have over the years been criticized as not being effective and a militia of government in power in the way they prosecute anti-corruption wars. The study evaluated the anti-graft agencies and their governance practices, their effectiveness in addressing the cankerworm in the country by employing the purposive sampling technique where 400 copies of questionnaires were distributed to professional accountants, bankers, journalist and lawyers. The data collected were analyzed using both descriptive and inferential statistics. The study revealed inadequate capacity of the workforce, non-commitment to integrity, ethical values and the rule of law, lack of openness, lack of transparency and accountability. The study recommends amendment of the extant laws establishing the anti-graft agencies for operational efficiency in prosecution, responsiveness to the constitution and adequate and targeted training for officials.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.174
Threshold uncertainty score0.756

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.193
GPT teacher head0.473
Teacher spread0.280 · 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

Citations7
Published2019
Admission routes1
Has abstractyes

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