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Record W2563628632 · doi:10.20869/auditf/2016/144/1325

Accounting standards that appeal to the professional

2016· article· en· W2563628632 on OpenAlexaff
Alain Burlaud, Maria NICULESCU

Bibliographic record

VenueAudit Financiar · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsFrancophone University Association
Fundersnot available
KeywordsAccountingComparabilityAuditTransparency (behavior)AppealAccounting standardProfessional conductPolitical scienceProfessional standardsBusinessFinancial accountingLawAccounting information systemEconomicsManagement

Abstract

fetched live from OpenAlex

The international accounting standards (IFRSs and ISAs) rely increasingly more on the "professional judgment".What is the situation in France and in Romania?After the conceptual clarifications, the article places the evolution of the professional judgment in the general movement of the law, which goes from "modernism" to "post-modernism" to become a law of specialists able to have a qualified opinion on highly technical subjects.In order to observe, in a scientific manner, this evolution of the accounting standards, we conducted a content analysis of principal legislative accounting texts, international and national (France and Romania), supplemented by a lexicometric analysis.These analyses allowed us to conclude that the importance of professional judgment in accounting standards is lower at the national level than it is at the international level.However, we highlight a number of dangers related to an increased use of professional judgment: loss of comparability and transparency, increased risks for accounting professionals including auditors, and significant discrepancies in the use of professional judgment in individual or consolidated accounts.

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)0.014
metaresearch head score (Gemma)0.059
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.014
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.059
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0040.014
Scholarly communication0.0090.004
Open science0.0000.004
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.009
GPT teacher head0.223
Teacher spread0.214 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations4
Published2016
Admission routes1
Has abstractyes

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