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Les caractéristiques du conseil d'administration et la gestion stratégique des résultats: une étude menée auprés des sociétés d‘État du Canada

2007· article· fr· W2082496148 on OpenAlexaffabout
Richard Bozec

Bibliographic record

VenueCanadian Public Administration · 2007
Typearticle
Languagefr
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsPolitical scienceHumanitiesArt

Abstract

fetched live from OpenAlex

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.

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)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.721
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0030.004
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.274
Teacher spread0.252 · 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.

Study designNot applicable
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

Citations1
Published2007
Admission routes2
Has abstractyes

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