Bibliographic record
Abstract
The definition of earnings management has been inconsistent in the literature. Major problems with the definition include ambiguity and immeasurability. As a solution, this paper intends to develop a constructive definition of earnings management and discuss the conceptual distinctions between earnings management and its counterparts. The review of the literature provides evidence of the validity of the developed definitions. The results of the paper are important for both theoretical and empirical researches on earnings management, as well as for regulators, lawmakers, firms’ contracting parties and investors. Keywords: earnings, management, manipulation, fraud Resume: La definition du management des revenues a ete contradictoire dans la litterature. Les problemes majeurs sur la definition comprend l’ambiguete et immeasurabilite. En tant que solution, ce documentr a l’ambition de developper une definition constructive du management des revenues et de discuter sur les distinctions conceptionelles entre le management des revenues et ses contreparites. La revue de la litterature sert le temoingnage de la validite de definitions developpees..Les resultat de ce document sont importants pour les recherches theoriques et empiriques sur le management des revenues, aussi pour les regulateurs, legislateurs, les parties contractuelles et d’investissements des entreprises. Mots-cles: earnings,management,manipulation,fraud (GAAP) in the United States. Every country has its own
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".