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Record W4313403010 · doi:10.1111/1911-3838.12329

Herbal Pharma Inc.: Conducting an Effective Group Audit*

2022· article· en· W4313403010 on OpenAlexaffvenue
Joanne Jones, Sandra Iacobelli, Sandra Scott

Bibliographic record

VenueAccounting Perspectives · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsUniversity of GuelphYork University
Fundersnot available
KeywordsAuditAccountingBusinessContext (archaeology)External auditorAuditor's reportInternal audit

Abstract

fetched live from OpenAlex

ABSTRACT As the relative weight of global economic activity continues to shift toward non‐OECD countries (OECD 2018), audit firms are more likely to encounter clients with significant business operations in foreign jurisdictions. The associated need to engage and oversee local component auditors in these jurisdictions can lead to challenges arising from different business cultures and the resulting intra‐audit miscommunications. Audit deficiencies related to these challenges have been detected by regulators (PCAOB 2011, 2010; CPAB 2012, 2015). Standard setters such as the IAASB and the Auditing and Assurance Standards Board (AASB) have responded by issuing an exposure draft proposing revisions to ISA 600 (IAASB 2020) and CAS 600 (AASB 2020) to strengthen the auditor's approach and provide enhanced guidance to practitioners. In light of this evolving area of assurance, this case was developed to deepen students' understanding of both group and component audits in an international context. The case takes the perspective of the group auditor and features an audit senior in a specialized role overseeing the component audit of a client's increasingly material Chinese subsidiary. Deficiencies in the prior year component audit, along with a change in the component auditor, further underlines the importance of robust risk analysis for the upcoming engagement.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.679
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0010.004
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.014
GPT teacher head0.246
Teacher spread0.232 · 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 designNot applicable
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

Citations0
Published2022
Admission routes2
Has abstractyes

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