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Record W4408865652 · doi:10.2308/horizons-2023-057

Technology and Its Implications for Staff Auditors

2025· article· en· W4408865652 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

VenueAccounting Horizons · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBig Data and Business Intelligence
Canadian institutionsUniversity of OttawaYork UniversityUniversity of WaterlooWilfrid Laurier University
Fundersnot available
KeywordsAccountingAuditBusiness

Abstract

fetched live from OpenAlex

SYNOPSIS To increase audit quality and decrease costs, audit firms have adopted technology to reduce routine tasks and increase the sophistication of data analysis. As a result, the Staff Auditors (SA) function has undergone a major shift requiring SA to be involved in more complex tasks involving higher level analysis, judgment, and greater technical skills. In addition, because technology has reduced the size, composition, and duration of time which the audit team spends at the client premises, the traditional on-the-job learning model of SA involving extensive interaction with senior audit team members has been altered. SA are by default becoming the face of the auditor at the client’s premises. Firms and educators need to rethink the nature of the training of SA and address the education and skill set of the students entering the audit profession. We discuss the implications, opportunities, and challenges of these changes for both firms and educators. JEL Classifications: M42; M40.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.879
Threshold uncertainty score0.626

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.032
GPT teacher head0.300
Teacher spread0.268 · 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