Proposition d’une nouvelle approche de la relation entre la taille de l’auditeur et la qualité de l’audit : l’importance de la technologie d’audit
Bibliographic record
Abstract
Dans cet article, nous utilisons le modèle des coûts fixes endogènes de Sutton (1991) pour proposer un cadre d’analyse nouveau pour envisager la relation entre la taille de l’auditeur et la qualité de l’audit et expliquer la structure duale et concentrée actuelle du marché de l’audit, opposant Big 4 et non-Big 4. Un des éléments-clés du modèle proposé est le rôle central que joue la technologie d’audit (investissements qui améliorent la qualité et/ou le processus de production) dans la détermination de la qualité (réelle et/ou perçue) et des honoraires d’audit. Nous suggérons que Big 4 et non-Big 4 diffèrent fondamentalement par leurs stratégies d’investissement en technologie. Les Big 4 s’engagent dans une « course aux investissements » qui mène à un oligopole naturel, alors qu’un grand nombre de non-Big 4, offrant des services de qualité moindre, subsistent en répondant à la demande des clients ne pouvant ou ne voulant recourir aux Big 4.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".