Manipulation comptable:les dirigeants et les autres parties prenantes
Bibliographic record
Abstract
(VF)Dans cet article, nous avançons que les dirigeants, soucieux de maximiser la richesse des actionnaires, manipulent l'information comptable afin de transférer de la richesse de certaines parties prenantes vers les actionnaires. En particulier, la divulgation d'une image de situation financière différente de l'image fidèle permet aux dirigeants d'optimiser les bénéfices futurs, grâce à une réduction des coûts fiscaux, sociaux, et financiers et à une maximisation des revenus futurs.(VA) In this article, we argue that managers, following an objective of maximizing shareholders' wealth, manipulate accounting information to transfer wealth from other stakeholders to the benefit of shareholders. In particular, not presenting a true and fair view of the financial situation of the firm allows the managers to optimizing the future benefits through a reduction of fiscal, social and financing costs and a maximization of the future incomes.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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.
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".