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Record W3119779545 · doi:10.51594/ijmer.v2i7.185

THE GROWING ISSUE OF BUSINESS FRAUD IN BURKINA FASO: WHAT BEST PREVENTION DEVICE?

2021· article· en· W3119779545 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Management & Entrepreneurship Research · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicOrganizational Management and Leadership
Canadian institutionsSt. Thomas University
Fundersnot available
KeywordsBusinessInternal controlControl (management)Constructive fraudState (computer science)Computer securityPublic relationsAccountingRisk analysis (engineering)Computer sciencePolitical science

Abstract

fetched live from OpenAlex

This study shows the state of fraud in businesses in Burkina Faso while diagnosing anti-fraud schemes. Thanks to this research carried out on Burkinabè companies, the results show that fraud affects all sectors of activity. It also exposes the limits of anti-fraud systems, which are essentially: the limits of the organizational framework, the weakness of the internal control system and the lack of an anti-fraud culture. To conduct our research, a quantitative approach is used to collect and interpret the data and the qualitative approach to deepen the analyzes. The results show that fraud is very real and affects the majority of companies: among the causes are weaknesses in internal control systems. To do this, we have proposed ways to identify the risks of fraud, thwart them and prevent them. This concerns particularly the mapping of fraud risks and the implementation of the anti-fraud system.Keywords: Fraud, Business, Prevention, Systems.

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.

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 categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.620
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.004
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.072
GPT teacher head0.341
Teacher spread0.269 · 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