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Record W1841082934 · doi:10.1111/auar.12039

Assessing Conformity with Generally Accepted Accounting Principles Using Expert Accounting Witness Evidence and the <i>Conceptual Framework</i>

2014· article· en· W1841082934 on OpenAlexaff
Russell Craig, Wally Smieliauskas, Joel Amernic

Bibliographic record

VenueAustralian Accounting Review · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAccountingAccounting researchForensic accountingConceptual frameworkFinancial accountingObjectivity (philosophy)ImpartialityAccounting standardWitnessPositive accountingConformityBusinessAccounting information systemPolitical scienceAuditSociologyLawEpistemology

Abstract

fetched live from OpenAlex

To enhance understanding of the status of the Financial Accounting Standards Board's Conceptual Framework for Financial Reporting, we analyse important rules of evidence in United States (US) courts regarding the presentation of expert accounting witness testimony. We draw on this analysis to recommend the relocation of the Conceptual Framework in the US Generally Accepted Accounting Principles (GAAP) hierarchy. For empirical support, we explore how rules of evidence in the criminal trial in 2006 of Enron's two most senior executives affected assessment of whether Enron's financial reports conformed with the FASB's GAAP. We recommend that the FASB's Conceptual Framework should be included in authoritative literature as the uppermost authority, and that it be grounded closely in user needs and the ethical principles associated with meeting those needs. Further, we recommend that accounting expert witnesses adopt an overriding concern for objectivity and impartiality in assisting courts to understand complex accounting matters within the Conceptual Framework.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.360
metaresearch head score (Gemma)0.593
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.360
Threshold uncertainty score0.789

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3600.593
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0250.013
Science and technology studies0.0050.020
Scholarly communication0.0210.023
Open science0.0060.012
Research integrity0.0060.006
Insufficient payload (model declined to judge)0.0030.001

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 head0.059
GPT teacher head0.302
Teacher spread0.243 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2014
Admission routes1
Has abstractyes

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