Cosmetic Earnings Management before and after Corporate Governance Legislation in Canada
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".