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Record W2608077326 · doi:10.2308/ajpt-51784

Is Operational Control Risk Informative of Financial Reporting Deficiencies?

2017· article· en· W2608077326 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

VenueAuditing A Journal of Practice & Theory · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsControl (management)Operational riskAuditBusinessAccountingInternal controlFinanceRisk managementFinancial riskActuarial scienceAudit riskRisk ControlRisk analysis (engineering)Computer science

Abstract

fetched live from OpenAlex

SUMMARY This study provides evidence concerning the significance of assessing operational control risk as part of an integrative evaluation of internal controls. We examine whether operational control risk indicators can be used as cues to potential unreported financial reporting control weaknesses and financial reporting deficiencies. We use data breaches and an operational control risk index, created through textual analysis of Form 10-Ks, as our two primary indicators of operational control risk. We find positive relations between our operational control risk indicators and future financial reporting control weaknesses, restatements, SEC comment letters, and audit fees, even after controlling for contemporaneous financial reporting control weaknesses. These findings suggest that operational control risk is informative of potential financial reporting deficiencies. Data Availability: Breach data are available subject to the approval of the Identity Theft Resource Center. All other data are publicly available from the sources identified in the article.

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)0.012
metaresearch head score (Gemma)0.477
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.464
Threshold uncertainty score0.892

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.477
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.008
Open science0.0010.000
Research integrity0.0000.001
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 head0.015
GPT teacher head0.270
Teacher spread0.256 · 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