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Record W2626519571 · doi:10.5430/afr.v7n1p214

Forensic Accounting Education in the UAE

2018· article· en· W2626519571 on OpenAlexvenueno aff
M. Ganga Bhavani, Anupam Mehta

Bibliographic record

VenueAccounting and Finance Research · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMarketing buzzForensic accountingCurriculumAccountingGlobeBusinessPublic relationsAccreditationOrder (exchange)Political scienceMedical educationAuditPsychologyFinancePedagogyMedicine

Abstract

fetched live from OpenAlex

This paper presents the current scenario of offerings and availability of forensic accounting education in universities in the UAE. This study is useful in gaining a complete understanding of available courses on forensic accounting at the graduate and postgraduate level, especially in accounting specialization. The results will help provide an insight into the direction of forensic accounting education in the UAE, where developing and improving forensic accounting education offerings has created serious buzz across the globe. Because of the increasing number of various corporate scandals all over the world, forensic accounting education has become the order of the day, and every accounting student needs to be trained in this field and every university has to offer it as part of the curriculum. The results of this study show that very few universities in the UAE offer and focus on this course as part of their curriculum in graduate and postgraduate levels. This study takes into consideration the current scenario of this course’s offerings among UAE universities, and the second outcome of this paper provides a comparison of course contents with the recommendations of the National Institute of Justice (NIJ) in the USA.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.661
Threshold uncertainty score0.813

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
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 head0.034
GPT teacher head0.325
Teacher spread0.290 · 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

Citations8
Published2018
Admission routes1
Has abstractyes

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