Les caractéristiques du conseil d'administration et la gestion stratégique des résultats: une étude menée auprés des sociétés d‘État du Canada
Bibliographic record
Abstract
Sommaire: L'objectif de cette étude est d'examiner l'efficacité du conseil d'admini tration des sociétés d'État a prévenir le lissage des bénéfices par les dirigeants. Les résultats de l'étude suggérent que la structure et la composition du conseil d'administration ont un impact sur la propension des dirigeants à lisser les bénéfices. Plus precisement, il apparait que plus les membres du conseil d'administration sont nombreux, plus grand est le risque pour les dirigeants de lisser les bénéfices. Ce risque croît aussi avec la présence au conseil d'administration de membres externes, c membres provenant de la fonction publique et lorsque les fonctions de President du conseil d'administration et de pdg sont occupées par une même personne. Enfin, lissage des bénéfices est réduit en présence d'un comié de vérification. Dans l'ensemble, sauf pour la question qui conceme l'indépendance des membres du conseil d'administration, ces résultats vont dans le sens des réformes actuelles sur gouvernance des sociétés en général et des sociétés d'État en particulier. Abstract: The objective of the study is to examine the effectiveness of the board directors of state‐owned enterprises (soes) in preventing managers from engaging earnings management. The results of the study suggest that board structure has an impact on earnings management activities. More precisely, it appears that soes with a large board are less inclined to curb managers from engaging in income smoothing than are soes with a small board. Also, the extent of income‐smoothing is great when the proportion of outsiders on the board increases, when the board is mo dependent on the political process, and when the roles of chair and ceo are held by the same person. Finally, the presence of an audit committee helps to prevent eamings‐smoothing by managers. in general, excluding the issue particular to the independence of board members, the results are in line with recent reforms in corpora governance.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".