Une approche de la théorie institutionnelle à la réglementation de l'audit légale et statutaire
Bibliographic record
Abstract
As a framework of analysis, Institutional Theory has been used to explain the various ways that organizations develop and change through time in response to various kinds of institutional pressure. This paper contributes to the Institutional Theory literature in several ways. First, the focus of the paper is on recent changes in the regulatory structures for "statutory auditing", an important function in contemporary capitalism. Second, we extend Institutional Theory through an international comparative analysis of changes in the regulatory structures for statutory auditing in the United States, France and Canada. Third, in a manner similar to Dillard et al. (2004), we extend Institutional Theory through an increased focus on the political aspects of institutional change with regard to the regulation of statutory auditing. Our overall finding is that there has been significant change in the regulatory structures for statutory auditing in recent years, resulting in increased levels of institutional isomorphism. Pressures, primarily external to the domestic regulatory frameworks, have resulted in greater legalism in the regulation of statutory auditing in the three countries examined. Globalization of international capital markets partly explains this phenomenon, but coercive isomorphism and mimetic isomorphism are also seen as playing important roles in this process. Key words:
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".