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Record W2155911808 · doi:10.5430/afr.v3n4p105

Cosmetic Earnings Management before and after Corporate Governance Legislation in Canada

2014· article· en· W2155911808 on OpenAlexvenueaboutno aff
Charles E. Jordan, Stanley J. Clark, Marilyn A. Waldron

Bibliographic record

VenueAccounting and Finance Research · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsnot available
Fundersnot available
KeywordsLegislationCorporate governanceAccountingBusinessEarningsPolitical scienceLawFinance

Abstract

fetched live from OpenAlex

Cosmetic earnings management (CEM) occurs when income is rounded (manipulated) up by a relatively small increment to reach a key reference point for users, which creates a disproportionately more favorable view of the company than would have existed otherwise. Significant research shows this type of biased reporting occurred virtually worldwide prior to the corporate governance legislation enacted in many countries in the early-to-mid 2000s. However, more recent research provides evidence that CEM in the U.S. disappeared after implementation of the Sarbanes-Oxley Act (SOX) of 2002. Yet, outside the U.S., little research exists on this form of earnings management following the enactment of similar corporate governance legislation. Using Benford’s Law and digital analysis, the current study tests for CEM in Canada before and after Ontario Bill 198 and other corporate governance legislation in that country. The results reveal clear signs of CEM in Canada prior to this legislation but no indications of it afterward, thus suggesting the legislation contributed to the eradication of CEM in Canada much like SOX did in the U.S.

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 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)0.002
metaresearch head score (Gemma)0.002
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 score0.236
Threshold uncertainty score0.971

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.013
GPT teacher head0.226
Teacher spread0.213 · 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 teacher head, not a consensus.

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

Citations2
Published2014
Admission routes2
Has abstractyes

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