MétaCan
Menu
Back to cohort
Record W2886331547 · doi:10.1111/abac.12138

Demand for and Assessment of Audit Quality in Private Companies

2018· article· en· W2886331547 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueAbacus · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAuditBusinessAccountingFinancial statementQuality auditInternal auditAudit planIncentiveInformation technology auditJoint auditQuality (philosophy)Audit riskExternal auditorVariety (cybernetics)Economics

Abstract

fetched live from OpenAlex

Financial statement audits are mandated in most countries, thus making it difficult to distinguish between auditing driven by private incentives versus that driven by regulation. Who would ask for an audit, and how would its quality be assessed in the absence of regulation? Many private companies in Canada get their financial statements audited even though the law does not require it. In this field study, we conduct interviews to discover reasons for demanding an audit, and criteria used to assess their quality. Our study reveals that both internal stakeholders (management, boards, and employees) as well as external stakeholders (customers, banks, and private equity firms) request audits. Users evaluate audit quality based on a variety of criteria such as the auditor's accounting expertise, the absence of errors, the fees involved, risk assessments offered, allocation of effort, internal control, and general business advice. Implications for audit regulations are discussed.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.218
Threshold uncertainty score0.440

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.024
GPT teacher head0.297
Teacher spread0.273 · 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