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Record W2765379593 · doi:10.1108/jaee-11-2016-0101

State of government accounting in Ghana and Benin: a “tentative” account

2017· article· en· W2765379593 on OpenAlexaff
Philippe Lassou

Bibliographic record

VenueJournal of Accounting in Emerging Economies · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAccounting and Organizational Management
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsClientelismIndigenousGovernment (linguistics)Corporate governanceOriginalityCivil servantsState (computer science)AccountingCivil societyValue (mathematics)Good governancePolitical sciencePublic administrationBusinessSociologySocial sciencePoliticsQualitative researchFinanceLawDemocracy

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to examine the state of government accounting in Ghana and Benin using neo-patrimonial and organizational façade lenses. Design/methodology/approach The study used two country case studies that engaged with stakeholders including donors, civil society, politicians, and civil servants. Semi-structured interviews were used as the main data collection technique, which were complemented by document analysis. Findings The study finds that government accounting reforms are decoupled and used in both countries as a façade which is caused, to a varying degree, by indigenous neo-patrimonial governance traits of informal institutions, patronage, and clientelism. And despite the relatively superior Ghanaian system, in terms of its functioning, compared to the Beninese, government accounting plays a more symbolic role in the former than in the latter. Originality/value This is one of the very few theoretically informed empirical studies that examine the state of government accounting in the two major African settings – Anglophone and Francophone. The results inform policies more tailored to indigenous governance issues for better outcomes.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.005
Open science0.0010.001
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 head0.011
GPT teacher head0.229
Teacher spread0.218 · 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.

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

Citations58
Published2017
Admission routes1
Has abstractyes

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